{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "   #  Regular Grid Variogram in Python for Engineers and Geoscientists \n",
    "\n",
    "## with GSLIB's GAM Program Converted to Python\n",
    "\n",
    "### Michael Pyrcz, Associate Professor, University of Texas at Austin \n",
    "\n",
    "\n",
    "#### Contacts: [Twitter/@GeostatsGuy](https://twitter.com/geostatsguy) | [GitHub/GeostatsGuy](https://github.com/GeostatsGuy) | [www.michaelpyrcz.com](http://michaelpyrcz.com) | [GoogleScholar](https://scholar.google.com/citations?user=QVZ20eQAAAAJ&hl=en&oi=ao) | [Book](https://www.amazon.com/Geostatistical-Reservoir-Modeling-Michael-Pyrcz/dp/0199731446)\n",
    "\n",
    "This is a tutorial for / demonstration of **Regular Grid Variogram Calculation in Python with GSLIB's GAM program translated to Python, wrappers and reimplementations of other GSLIB: Geostatistical Library methods** (Deutsch and Journel, 1997). \n",
    "\n",
    "This exercise demonstrates the semivariogram calculation method in Python with wrappers and reimplimentation of GSLIB methods.  The steps include:\n",
    "\n",
    "1. generate a 2D model with sequential Gaussian simulation\n",
    "2. calculate and visualize experimental semivariograms\n",
    "\n",
    "To accomplish this I have provide wrappers or reimplementation in Python for the following GSLIB methods:\n",
    "\n",
    "1. sgsim - sequantial Gaussian simulation limited to 2D and unconditional\n",
    "2. hist - histograms plots reimplemented with GSLIB parameters using python methods\n",
    "3. locmap - location maps reimplemented with GSLIB parameters using python methods\n",
    "4. pixelplt - pixel plots reimplemented with GSLIB parameters using python methods\n",
    "5. locpix - my modification of GSLIB to superimpose a location map on a pixel plot reimplemented with GSLIB parameters using Python methods\n",
    "5. affine - affine correction adjust the mean and standard deviation of a feature reimplemented with GSLIB parameters using Python methods\n",
    "\n",
    "I have also started to translate the GSLIB support subfunctions to Python.  Stay tuned.\n",
    "\n",
    "The GSLIB source and executables are available at http://www.statios.com/Quick/gslib.html.  For the reference on using GSLIB check out the User Guide, GSLIB: Geostatistical Software Library and User's Guide by Clayton V. Deutsch and Andre G. Journel.  Overtime, more of the GSLIB programs will be translated to Python and there will be no need to have the executables.  For this workflow you will need sgsim.exe from GSLIB.com for windows and Mac OS executables from https://github.com/GeostatsGuy/GSLIB_MacOS.  \n",
    "\n",
    "I did this to allow people to use these GSLIB functions that are extremely robust in Python. Also this should be a bridge to allow so many familar with GSLIB to work in Python as a kept the parameterization and displays consistent with GSLIB.  The wrappers are simple functions declared below that write the parameter files, run the GSLIB executable in the working directory and load and visualize the output in Python. This will be included on GitHub for anyone to try it out https://github.com/GeostatsGuy/.  \n",
    "\n",
    "This was my first effort to translate the GSLIB Fortran to Python.  It was pretty easy so I'll start translating other critical GSLIB functions.  I've completed NSCORE, DECLUS and GAM as of now.\n",
    "\n",
    "#### Load the required libraries\n",
    "\n",
    "The following code loads the required libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os                                                 # to set current working directory \n",
    "import numpy as np                                        # arrays and matrix math\n",
    "import pandas as pd                                       # DataFrames\n",
    "import matplotlib.pyplot as plt                           # plotting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you get a package import error, you may have to first install some of these packages. This can usually be accomplished by opening up a command window on Windows and then typing 'python -m pip install [package-name]'. More assistance is available with the respective package docs.  \n",
    "\n",
    "#### Declare functions\n",
    "\n",
    "Here are the wrappers and reimplementations of GSLIB method along with two utilities to load GSLIB's Geo-EAS from data files into DataFrames and 2D Numpy arrays.  These are used in the testing workflow.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Some GeostatsPy Functions - by Michael Pyrcz, maintained at https://git.io/fNgR7.\n",
    "# A set of functions to provide access to GSLIB in Python.\n",
    "# GSLIB executables: nscore.exe, declus.exe, gam.exe, gamv.exe, vmodel.exe, kb2d.exe & sgsim.exe must be in the working directory \n",
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt                          \n",
    "import random as rand\n",
    "image_type = 'tif'; dpi = 600\n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a 1D or 2D numpy ndarray for use with Python methods\n",
    "def GSLIB2ndarray(data_file,kcol,nx,ny): \n",
    "    colArray = []\n",
    "    if ny > 1:\n",
    "        array = np.ndarray(shape=(ny,nx),dtype=float,order='F')\n",
    "    else:\n",
    "        array = np.zeros(nx)    \n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])          # get the number of columns\n",
    "        for icol in range(0, ncol):   # read over the column names\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            if icol == kcol:\n",
    "                col_name = head[0].split()[0]       \n",
    "        if ny > 1:\n",
    "            for iy in range(0,ny):\n",
    "                for ix in range(0,nx):\n",
    "                    head = [next(myfile) for x in range(1)]\n",
    "                    array[ny-1-iy][ix] = head[0].split()[kcol]\n",
    "        else:\n",
    "            for ix in range(0,nx):\n",
    "                head = [next(myfile) for x in range(1)]\n",
    "                array[ix] = head[0].split()[kcol]\n",
    "    return array,col_name \n",
    "\n",
    "# utility to convert GSLIB Geo-EAS files to a pandas DataFrame for use with Python methods\n",
    "def GSLIB2Dataframe(data_file):\n",
    "    colArray = []\n",
    "    with open(data_file) as myfile:   # read first two lines\n",
    "        head = [next(myfile) for x in range(2)]\n",
    "        line2 = head[1].split()\n",
    "        ncol = int(line2[0])\n",
    "        for icol in range(0, ncol):\n",
    "            head = [next(myfile) for x in range(1)]\n",
    "            colArray.append(head[0].split()[0])\n",
    "        data = np.loadtxt(myfile, skiprows = 0)\n",
    "        df = pd.DataFrame(data)\n",
    "        df.columns = colArray\n",
    "        return df\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods, displayed and as image file\n",
    "def hist(array,xmin,xmax,log,cumul,bins,weights,xlabel,title,fig_name):\n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency')  \n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return\n",
    "\n",
    "# histogram, reimplemented in Python of GSLIB hist with MatPlotLib methods (version for subplots)\n",
    "def hist_st(array,xmin,xmax,log,cumul,bins,weights,xlabel,title):  \n",
    "    cs = plt.hist(array, alpha = 0.2, color = 'red', edgecolor = 'black', bins=bins, range = [xmin,xmax], weights = weights, log = log, cumulative = cumul)\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel); plt.ylabel('Frequency') \n",
    "    return\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods\n",
    "def locmap(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))    \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# location map, reimplemention in Python of GSLIB locmap with MatPlotLib methods (version for subplots)\n",
    "def locmap_st(df,xcol,ycol,vcol,xmin,xmax,ymin,ymax,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    ixy = 0   \n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, norm=None, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)    \n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return im           \n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods\n",
    "def pixelplt(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    print(str(step))\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    plt.figure(figsize=(8,6))\n",
    "    im = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels=np.linspace(vmin,vmax,100))\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    cbar = plt.colorbar(im,orientation = 'vertical',ticks=np.linspace(vmin,vmax,10))\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return im\n",
    "\n",
    "# pixel plot, reimplemention in Python of GSLIB pixelplt with MatPlotLib methods(version for subplots)\n",
    "def pixelplt_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    x = [];y = []; v = [] # use dummy since scatter plot controls legend min and max appropriately and contour does not!\n",
    "    cs = plt.contourf(xx,yy,array,cmap=cmap,vmin=vmin,vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(x,y,s=None, c=v, marker=None,cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.clim(vmin,vmax)\n",
    "    cbar = plt.colorbar(im, orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods\n",
    "def locpix(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap,fig_name):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    plt.figure(figsize=(8,6))\n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)\n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)  \n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "    plt.savefig(fig_name + '.' + image_type,dpi=dpi)\n",
    "    plt.show()\n",
    "    return cs\n",
    "\n",
    "# pixel plot and location map, reimplementation in Python of a GSLIB MOD with MatPlotLib methods(version for subplots)\n",
    "def locpix_st(array,xmin,xmax,ymin,ymax,step,vmin,vmax,df,xcol,ycol,vcol,title,xlabel,ylabel,vlabel,cmap):\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    ixy = 0 \n",
    "    cs = plt.contourf(xx, yy, array, cmap=cmap,vmin=vmin, vmax=vmax,levels = np.linspace(vmin,vmax,100))\n",
    "    im = plt.scatter(df[xcol],df[ycol],s=None, c=df[vcol], marker=None, cmap=cmap, vmin=vmin, vmax=vmax, alpha=0.8, linewidths=0.8, verts=None, edgecolors=\"black\")\n",
    "    plt.title(title)\n",
    "    plt.xlabel(xlabel)  \n",
    "    plt.ylabel(ylabel)\n",
    "    plt.xlim(xmin,xmax)\n",
    "    plt.ylim(ymin,ymax)\n",
    "    cbar = plt.colorbar(orientation = 'vertical')\n",
    "    cbar.set_label(vlabel, rotation=270, labelpad=20)\n",
    "\n",
    "# affine distribution correction reimplemented in Python with numpy methods \n",
    "def affine(array,tmean,tstdev): \n",
    "    mean = np.average(array)\n",
    "    stdev = np.std(array)  \n",
    "    array = (tstdev/stdev)*(array - mean) + tmean\n",
    "    return(array)   \n",
    "\n",
    "def make_variogram(nug,nst,it1,cc1,azi1,hmaj1,hmin1,it2=1,cc2=0,azi2=0,hmaj2=0,hmin2=0):\n",
    "    if cc2 == 0:\n",
    "        nst = 1\n",
    "    var = dict([('nug', nug), ('nst', nst), ('it1', it1),('cc1', cc1),('azi1', azi1),('hmaj1', hmaj1), ('hmin1', hmin1), \n",
    "      ('it2', it2),('cc2', cc2),('azi2', azi2),('hmaj2', hmaj2), ('hmin2', hmin2)])\n",
    "    if nug + cc1 + cc2 != 1:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: sill does not sum to 1.0, do not use in simulation \\x1b[0m')\n",
    "    if cc1 < 0 or cc2 < 0 or nug < 0 or hmaj1 < 0 or hmaj2 < 0 or hmin1 < 0 or hmin2 < 0:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: contributions and ranges must be all positive \\x1b[0m')\n",
    "    if hmaj1 < hmin1 or hmaj2 < hmin2:\n",
    "        print('\\x1b[0;30;41m make_variogram Warning: major range should be greater than minor range \\x1b[0m')\n",
    "    return var    \n",
    "\n",
    "# sequential Gaussian simulation, 2D unconditional wrapper for sgsim from GSLIB (.exe must be in working directory)\n",
    "def GSLIB_sgsim_2d_uncond(nreal,nx,ny,hsiz,seed,var,output_file):\n",
    "    import os\n",
    "    import numpy as np \n",
    "    \n",
    "    nug = var['nug']\n",
    "    nst = var['nst']; it1 = var['it1']; cc1 = var['cc1']; azi1 = var['azi1']; hmaj1 = var['hmaj1']; hmin1 = var['hmin1'] \n",
    "    it2 = var['it2']; cc2 = var['cc2']; azi2 = var['azi2']; hmaj2 = var['hmaj2']; hmin2 = var['hmin2']     \n",
    "    max_range = max(hmaj1,hmaj2) \n",
    "    hmn = hsiz * 0.5   \n",
    "    hctab = int(max_range/hsiz)*2 + 1\n",
    "\n",
    "    sim_array = np.random.rand(nx,ny)\n",
    "\n",
    "    file = open(\"sgsim.par\", \"w\")\n",
    "    file.write(\"              Parameters for SGSIM                                         \\n\")\n",
    "    file.write(\"              ********************                                         \\n\")\n",
    "    file.write(\"                                                                           \\n\")\n",
    "    file.write(\"START OF PARAMETER:                                                        \\n\")\n",
    "    file.write(\"none                          -file with data                              \\n\")\n",
    "    file.write(\"1  2  0  3  5  0              -  columns for X,Y,Z,vr,wt,sec.var.          \\n\")\n",
    "    file.write(\"-1.0e21 1.0e21                -  trimming limits                           \\n\")\n",
    "    file.write(\"0                             -transform the data (0=no, 1=yes)            \\n\")\n",
    "    file.write(\"none.trn                      -  file for output trans table               \\n\")\n",
    "    file.write(\"1                             -  consider ref. dist (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"none.dat                      -  file with ref. dist distribution          \\n\")\n",
    "    file.write(\"1  0                          -  columns for vr and wt                     \\n\")\n",
    "    file.write(\"-4.0    4.0                   -  zmin,zmax(tail extrapolation)             \\n\")\n",
    "    file.write(\"1      -4.0                   -  lower tail option, parameter              \\n\")\n",
    "    file.write(\"1       4.0                   -  upper tail option, parameter              \\n\")\n",
    "    file.write(\"0                             -debugging level: 0,1,2,3                    \\n\")\n",
    "    file.write(\"nonw.dbg                      -file for debugging output                   \\n\")\n",
    "    file.write(str(output_file) + \"           -file for simulation output                  \\n\")\n",
    "    file.write(str(nreal) + \"                 -number of realizations to generate          \\n\")\n",
    "    file.write(str(nx) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(str(ny) + \" \" + str(hmn) + \" \" + str(hsiz) + \"                              \\n\")\n",
    "    file.write(\"1 0.0 1.0                     - nz zmn zsiz                                \\n\")\n",
    "    file.write(str(seed) + \"                  -random number seed                          \\n\")\n",
    "    file.write(\"0     8                       -min and max original data for sim           \\n\")\n",
    "    file.write(\"12                            -number of simulated nodes to use            \\n\")\n",
    "    file.write(\"0                             -assign data to nodes (0=no, 1=yes)          \\n\")\n",
    "    file.write(\"1     3                       -multiple grid search (0=no, 1=yes),num      \\n\")\n",
    "    file.write(\"0                             -maximum data per octant (0=not used)        \\n\")\n",
    "    file.write(str(max_range) + \" \" + str(max_range) + \" 1.0 -maximum search  (hmax,hmin,vert) \\n\")\n",
    "    file.write(str(azi1) + \"   0.0   0.0       -angles for search ellipsoid                 \\n\")\n",
    "    file.write(str(hctab) + \" \" + str(hctab) + \" 1 -size of covariance lookup table        \\n\")\n",
    "    file.write(\"0     0.60   1.0              -ktype: 0=SK,1=OK,2=LVM,3=EXDR,4=COLC        \\n\")\n",
    "    file.write(\"none.dat                      -  file with LVM, EXDR, or COLC variable     \\n\")\n",
    "    file.write(\"4                             -  column for secondary variable             \\n\")\n",
    "    file.write(str(nst) + \" \" + str(nug) + \"  -nst, nugget effect                          \\n\")\n",
    "    file.write(str(it1) + \" \" + str(cc1) + \" \" +str(azi1) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj1) + \" \" + str(hmin1) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")\n",
    "    file.write(str(it2) + \" \" + str(cc2) + \" \" +str(azi2) + \" 0.0 0.0 -it,cc,ang1,ang2,ang3\\n\")\n",
    "    file.write(\" \" + str(hmaj2) + \" \" + str(hmin2) + \" 1.0 - a_hmax, a_hmin, a_vert        \\n\")  \n",
    "    file.close()\n",
    "\n",
    "    os.system('\"sgsim.exe sgsim.par\"')       \n",
    "    sim_array = GSLIB2ndarray(output_file,0,nx,ny)         \n",
    "    return(sim_array[0])\n",
    "\n",
    "# extract regular spaced samples from a model   \n",
    "def regular_sample(array,xmin,xmax,ymin,ymax,step,mx,my,name):\n",
    "    x = []; y = []; v = []; iix = 0; iiy = 0;\n",
    "    xx, yy = np.meshgrid(np.arange(xmin, xmax, step),np.arange(ymax, ymin, -1*step))\n",
    "    iiy = 0\n",
    "    for iy in range(0,ny):\n",
    "        if iiy >= my:\n",
    "            iix = 0\n",
    "            for ix in range(0,nx):\n",
    "                if iix >= mx:\n",
    "                    x.append(xx[ix,iy]);y.append(yy[ix,iy]); v.append(array[ix,iy])\n",
    "                    iix = 0; iiy = 0\n",
    "                iix = iix + 1\n",
    "        iiy = iiy + 1\n",
    "    df = pd.DataFrame(np.c_[x,y,v],columns=['X', 'Y', name])\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's the GAM program translated to Python. Note: it was simplified to run just one experimental semivariogram at a time (in a simgle direction) and only on 2D grids. I have avoided loops and used NumPy ndarray operators and broadcast methods as much as possible for speed.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GSLIB's GAM program (Deutsch and Journel, 1998) converted from the original Fortran to Python \n",
    "# by Michael Pyrcz, the University of Texas at Austin (Jan, 2019)\n",
    "def gam(array,tmin,tmax,xsiz,ysiz,ixd,iyd,nlag,isill): \n",
    "# Parameters - consistent with original GSLIB    \n",
    "# array - 2D gridded data / model\n",
    "# tmin, tmax - property trimming limits\n",
    "# xsiz, ysiz - grid cell extents in x and y directions\n",
    "# ixd, iyd - lag offset in grid cells\n",
    "# nlag - number of lags to calculate\n",
    "# isill - 1 for standardize sill\n",
    "#\n",
    "# Set constants\n",
    "    if array.ndim == 2:\n",
    "        ny = (array.shape[0])\n",
    "        nx = (array.shape[1])\n",
    "    elif array.ndim == 1:\n",
    "        nx = len(array)     \n",
    "        ny = 1\n",
    "\n",
    "    nvarg = 1   # for mulitple variograms repeat the program\n",
    "    nxy = nx*ny\n",
    "    mxdlv = nlag\n",
    "\n",
    "# Allocate the needed memory:   \n",
    "    lag = np.zeros(mxdlv)\n",
    "    vario = np.zeros(mxdlv)\n",
    "    hm = np.zeros(mxdlv)\n",
    "    tm = np.zeros(mxdlv)\n",
    "    hv = np.zeros(mxdlv)\n",
    "    npp = np.zeros(mxdlv)\n",
    "    ivtail = np.zeros(nvarg + 2)\n",
    "    ivhead = np.zeros(nvarg + 2)\n",
    "    ivtype = np.zeros(nvarg + 2)\n",
    "    ivtail[0] = 0; ivhead[0] = 0; ivtype[0] = 0;\n",
    "    \n",
    "# Summary statistics for the data after trimming\n",
    "    inside = ((array > tmin) & (array < tmax))\n",
    "    avg = array[(array > tmin) & (array < tmax)].mean()\n",
    "    stdev = array[(array > tmin) & (array < tmax)].std()\n",
    "    var = stdev**2.0\n",
    "    vrmin = array[(array > tmin) & (array < tmax)].min()\n",
    "    vrmax = array[(array > tmin) & (array < tmax)].max()\n",
    "    num = ((array > tmin) & (array < tmax)).sum()\n",
    "    \n",
    "# For the fixed seed point, loop through all directions:\n",
    "    for iy in range(0,ny):\n",
    "        for ix in range(0,nx):\n",
    "            if inside[iy,ix]:\n",
    "                vrt = array[iy,ix]\n",
    "                ixinc = ixd\n",
    "                iyinc = iyd\n",
    "                ix1   = ix\n",
    "                iy1   = iy\n",
    "                for il in range(0,nlag):\n",
    "                    ix1 = ix1 + ixinc\n",
    "                    if ix1 >= 0 and ix1 < nx:\n",
    "                        iy1 = iy1 + iyinc\n",
    "                        if iy1 >= 1 and iy1 < ny:\n",
    "                            if inside[iy1,ix1]:\n",
    "                                vrh = array[iy1,ix1]\n",
    "                                npp[il] = npp[il] + 1\n",
    "                                tm[il] = tm[il] + vrt\n",
    "                                hm[il] = hm[il] + vrh\n",
    "                                vario[il] = vario[il] + ((vrh-vrt)**2.0) \n",
    "\n",
    "# Get average values for gam, hm, tm, hv, and tv, then compute\n",
    "# the correct \"variogram\" measure:\n",
    "    for il in range(0,nlag):\n",
    "        if npp[il] > 0:\n",
    "            rnum   = npp[il]\n",
    "            lag[il] = np.sqrt((ixd*xsiz*il)**2+(iyd*ysiz*il)**2)\n",
    "            vario[il] = vario[il] / float(rnum)\n",
    "            hm[il]  = hm[il]  / float(rnum)\n",
    "            tm[il]  = tm[il]  / float(rnum)       \n",
    "\n",
    "# Standardize by the sill\n",
    "\n",
    "            if isill == 1:\n",
    "                vario[il] = vario[il] / var\n",
    "            \n",
    "# Semivariogram\n",
    "\n",
    "            vario[il] = 0.5 * vario[il]\n",
    "    return lag, vario, npp\n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here's a simple test of the GAM code with visualizations to check the results including the gridded data pixelplt, histogram and experimental semivariograms in 4 directions.\n",
    "\n",
    "#### Set the working directory\n",
    "\n",
    "I always like to do this so I don't lose files and to simplify subsequent read and writes (avoid including the full address each time).  Also, in this case make sure to place the required (see above) GSLIB executables in this directory or a location identified in the environmental variable *Path*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir(\"c:/PGE337\")                                   # set the working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will have to update the part in quotes with your own working directory and the format is different on a Mac (e.g. \"~/PGE\").  \n",
    "\n",
    "##### Demonstrations / Benchmarks\n",
    "\n",
    "The following are the basic parameters for the demonstration.  This includes the number of cells in the 2D regular grid, the cell size (step) and the x and y min and max along with the color scheme.\n",
    "\n",
    "Then we make a single realization of a Gausian distributed feature over the specified 2D grid and then apply affine correction to ensure we have a reasonable mean and spread for our feature's distribution, assumed to be Porosity (e.g. no negative values) while retaining the Gaussian distribution.  Any transform could be applied at this point.  We are keeping this workflow simple. *This is our truth model that we will analyze*.\n",
    "\n",
    "The parameters of *GSLIB_sgsim_2d_uncond* are (nreal,nx,ny,hsiz,seed,variogram,azi,output_file).  nreal is the number of realizations, nx and ny are the number of cells in x and y, hsiz is the cell siz, seed is the random number seed, variogram is a custom object with all the variogram parameters and output_file is a GEO_EAS file with the simulated realization.  The ouput is the 2D numpy array of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nx = 100; ny = 100; cell_size = 10                               # grid number of cells and cell size\n",
    "xmin = 0.0; ymin = 0.0;                                          # grid origin\n",
    "xmax = xmin + nx * cell_size; ymax = ymin + ny * cell_size       # calculate the extent of model\n",
    "seed = 74073                                                     # random number seed  for stochastic simulation    \n",
    "range_max = 1800; range_min = 500; azimuth = 65                  # Porosity variogram ranges and azimuth\n",
    "vario = make_variogram(0.0,nst=1,it1=1,cc1=1.0,azi1=65,hmaj1=1800,hmin1=500)\n",
    "mean = 10.0; stdev = 2.0                                         # Porosity mean and standard deviation\n",
    "#cmap = plt.cm.RdYlBu\n",
    "vmin = 4; vmax = 16; cmap = plt.cm.plasma                        # color min and max and using the plasma color map\n",
    "\n",
    "# calculate a stochastic realization with standard normal distribution\n",
    "sim = GSLIB_sgsim_2d_uncond(1,nx,ny,cell_size,seed,vario,\"simulation\")\n",
    "sim = affine(sim,mean,stdev)                                     # correct the distribution to a target mean and standard deviation.\n",
    "\n",
    "plt.subplot(121)\n",
    "hist_st(sim.flatten(),vmin,vmax,log=False,cumul=False,bins=20,weights=None,xlabel='Porosity (%)',title='Porosity (%)') \n",
    "\n",
    "plt.subplot(122)\n",
    "pixelplt_st(sim,xmin,xmax,ymin,ymax,cell_size,vmin,vmax,'Porosity','X(m)','Y(m)','Porosity (5)',cmap)\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.2, hspace=0.2)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's calculate the variograms in the 000, 045, 090 and 135 azimuths.  Note 000 is up, along the Y axis and 090 is right along the X axis.  We will add the experimental variogram plot to the histogram and pixel plot of the 2D porosity model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jt9VUv+YLah2vcQ4CSOjRbc3dtkYJGEMwbp1x59FKuNdw75O/bmMomId6ajmbhchASFNvQ2bkEHQloLVb3DhAdn9jmnuE88tHa68jMhA8Z9S6H26OhdDBNQRzSvX4/dy437UhaC38tfnz8hB5jkD+PSF149zG2PcMCFrbc2sNP6/GuGO76wcAQcZfqzF2ewo/VgYQwo/UXTsghLa/j6J1juBow5sS8OBPH5/R98NLX7bIPPHE7Fr53X9f/1sA+sGqDxpjPriD3RIABwN4IWxrkXVEtNTsx7lVLgT6AgAvmuJ9//02Pj7+/F/4hV/YjaPbT3jySeDhh4GlS4HFi4df7+f84AfA+Hg9FU8+CXznO8DYGNDrxWnaWeb677d9UqQeddRRuPfee/f0MCKRyCwgov/e02OYJY8T0WHGmO+7cF4O89sC4MhguyWw/c+2APj11vovjjqw+8P4gwBw3CFPMzf96qtRlimkVBBCQyYaJDTy7gDZ2ABC6oYoEdL+kd5/cswJEkuS2QKXupL2pyboSkIkCklWIckqyKyCkApaSb+fzEqoIoWQCunYAOTOJRINkhqUKBh3TErsuU0lIccGkItsSzc9SO36UkAXid+meLKLcjKHUYSqSNF/sotBL0dVJF6YVUWKokgbc5RlZeO1UnZbKTWyzsBei5sHkbjxOoGotUDRy9Hb1kW/l6MsU/R6OXq9DrK0RJqV0FqgbJ2TqZREIptCKO8MkOcl0rQ5rtSNs9/rAAA63T5UMLdlkUJr0RibqiSKMoGqJLQWGAxSKC0ghUael0PXUikJVQnIRCORCmlW+vfDc1RKoijqPwMmexkGpX2dSI3F431I9/lSVR14pbRAUSSolEDS+JwZ5GkJmWgUReL3zdIS3a6979J9HoTQ9vMrtX0A4Y+hG/eSpIYJ3j/pM5fP6Pvhx0+k+PK/nDqTXTyLO7f3jTEnzXC3LQA+7kTpPUSkATwVtnDPfgcRHQ/bP/N0Y8wTo7YJv99OOukkE/9+mweuuAJYtgxYvrxet2EDsHEj8La37blx7SVs2ACcey7wgQ/Y12efbUXr7bfb1/xeOH2RYeb677d9UqRGIpHIHuBOAOcBuNz9vCNY/4dEtBa2cNJPnZD9DIC/4irAAF4G4OIdnsVYITJKoCZZ1RCoLE5JOmcrUagmcy9CQyHLQlUkCkIYL1BlVkIkGhIV1CCBVhLKiTWSGjKvvJAgqSE6VmCQE4LETmGiIfIKlFthTKWEHiTQRQJTSRgloCsBNUigigRVkaCYzNHf3kVvW9efD7ACVCsBpSSkE2FTQVJ7MV5fqxU92mW8VGWK3rYuiiJFWdpr63YHUJVEmpVIpMaofhWVE5ehgLP79tHtDhpjYyHKY0lVibJIURZp4z7Y40kgsT8rZZ3dbU+OYeDGphUhyyrIJLh/LOBHCNQsrUdfqXo7FqhSaChtBafSGtt7KRYfPECWVcicyNayFscsUNskUnnRrISA0gKo7L5FmUAIjUoJ5HnpHxj4a1YSZZFCJgpSKkg3JyxQlbvfM8VgAGW+M+P9doHbAbwYwBeJ6Nmw1Tj/Z3cOYG+BiJ4B4OMAVhtj/mtPj2e/ZpQQXb48qi7H8uXAunVWjD73uTYq5Pbb6+lZt87q+Thdu5coUiORSGSGENFNsC7oU4loC4C/gBWn64joAgDfA/DbbvNPwVb2fQjAJIDXAoAx5sdE9C4AG912lxpjpixCwoRhpzLRSNLSO4MAvCtlHSrphSoQOKaVhBbGiyNdyYaLyscJJYGuhkUJC0t+hxJVi1Inmvin/72U/nejBUwloQYJjBKoBimUE6dFP0PRy7Htp+PewRTSum9lkaLf63hXLsWwi8oCNhyDCscR4AVqyyll948RQnsxCMALOKS1Qzhqv/acAXZ++WFDm0oJL05VJdHr5V6gJlIh61ZDzq3dr75j/L6qJCrRFLP2uMKL1MwJZxaeB4wPMNYdIJGq4eryOXi7pCWu27CTClj3VgiNNCuhKumdVH7YsH17F6qS9uGAkl6kAkBRpA23dUYYgKYf5qyZ4nvgIwA+4tqyFADO21dDfae4/hQAjDEfAPAOAE8B8A9k48KrWTjTkci8ERrMy5cDa9YA73oX8JKXNAVp1PN7hihSI5FIZIYYY141xVsvGbGtAXDhFMf5COwftTM4t/0phBWo7MyJREFr8oJRB2LIi9Eg1FdrQuVCbsP1jXMpsqIqaYYOs3OqlUQ5mSM/oOfCfDUodduWLYFaCagq86/V9swLVFWk0JVA1cvQf3LMC9R+L/fCUSlp3TZYMemdvMQKIDjHksUNi7+qSFCOEHR8TKOEF6i6JV5loqyrGbzWhd0mDKFNpIZMlBexoajz+0rlroGdQedcTyP0yiLFYJBiUKZWnDpnk91ZFrHtcbObydsM+nnjfXZEASdMnVjlEOLF430ksr6eokyhKoFBmaIopL9mIQ20Incdxh1PurkaMQdOyFdKQDqxzOPh0GqGP2NK1eMP78WMMKM/37vKNN8D/3teTriXMc318/uvA/C63TScSGTGLFtm3dN16+zr978f6HaBe++1IcBRmO5ZokiNRCKRBYYQNoRVJNo7n0IY74aGhO4ogNoprSSQNMWb1gICgfOqJCSqhosqXbiuBgAX+qsGJdIDet5FBWDFqhOqJsxn3G6Fqh5YYWr3tyG+RS9vCNRBrymuOB+Tw0IhdUO4pFnpRWBIFTik7ffLIoVS0gs+FpsAfEg1i6kiEKBCaO8kplkgHAMXd5QAVUr6Bwh5Z+DFtGq4oBrbt3cx2ctRKYk8LW1+qxPGeW5/lkWKArAhtZrdzdEOazunNHRdKyW8QB3rFpCJdqHkVpSHApW3E9IgkQoVJLQiJFLZsOEg1Hg6+L5VToSqSnhHOnSXJ7ePYeAfpszcTSUANMVDmMjCoCxLbNmyBf1+f8cbLyA6nQ6WLFmCNB2d6x6ZfzjM96yzgKoCkgT4p3+y77F4jUJ1zxFFaiQSiSwgiKxLxX/IswhlgaoBCE0QwqByDplo/ZEehv2GCKEhhGmIWu1CL9k5pUrXhYdcQaVqkCKpCpjEClOGBSoXUQoL4Pj81lCgTuYNgRqG34YCVbtcxwIYci3bwtA6mHURJSm1L7qklfA5qNMhhIaC9D8bx/c5lDaPUikBoZpubns8UiqkKCGl9nm2KGqhKhOFvGNDhislkaWlF8/tENv2mNpFnColMenEfp5Onbsr3bErJZGh8nNcKdlwUAE4FzZ48CGND+2dTpxyri1gBT8A54pLL8JVZR1zqex94nsOzE6kzme4b2T3sGXLFixevBhHHXUUiPaNBw7GGDzxxBPYsmULjj766Lk/QSyUNCXtqVm+HJiYAO67D7jkkpiHujcRRWokEoksIIiMz0PVlQAS+BBUXTVzMQFXybZV9WeqkFSRKF9UiTGKfKGk5rbaCmL3Wg3q/064um8oStk15RxZraRbVwvU/nZbXbcqkkb4beiWai28o1gWKZDYy+Pw37ZArH9qJM7t5BxHH3IbuKWcK9lGJsqGqbZCgFMXfstzlkgNqeoCT41jSOWrDIfzr7WAke4eShsGyyG7OepwYj5vKPZ4bKjsKpszWo9vspdDK/LhuKMIha9WhKJIoISbey18SC9QF1nSilA5ccxOL7uvPMawmJNdKZG0hCaL6rD4FYtS/gyEYcGzIorUBU2/39+nBCoAEBGe8pSn4Ec/mqeiz2Ec6/Lldflajmvdj2lPzZVXAt/4BrB6NXD11XX+acxD3fNEkRqJRCILCTK1k8lC1aGURFUkqJD4qr8Ahr7pWdyOEqqjME6k0Aihw06gUaIWqq4QUkg5mVtX1okRdmarIkVVJL6Kr1LCV70NW5xwIaE2ZZF6gTMYpF7QaS2QpqUX7SR1I4zXF5gKRemI/xFDweqr/Cbw7WWEc2fD7fi3dgYlSY3UtWgJQ6OrIrHz2JqzdnsZkWkflsyCNZHau5JCaC9UAaAoEhSFRCKHHU4Wnjy/oYgtisS/1or8fQhzUO2+yrfBYYEqws9c1XQ/VWVDhzlkGIAPLW6Ldv4M2ArHTpzP1kndJ8sW7V/sSwKVmddrCsvVrllj1VeMXQXQnJrTTwduuAF473uBN7+5qeXjVO15ZlkuLxKJRCJ7Ct9CpSVQjXOdyjJF6dq4VGWK0uV/8iIS6yqycyqS6fMH/TmKushRu9qvrcxbL+Vk7oWpbVuT+EJMLFBVkaAapNZF7eUoXH4o9ym1RXsSFGXixZnNr5TeYeNwUFVJL2yF0Mg7A9t+JSvrcGXdFLpSKnS6fXS6A3S6db5b2MaHCzZpbV3UPC8xPt7zx7fb1K4s/wxFOjkXt90zFRiuNAxMXSCIxy4T1VhG9UhVlf09y5QPx23DVYQBK0Z5qZRAUUgUhcSgTLxATaQ9Vpa5Ik6uDc5UDzvqEOVAwLsx5rl1X/m92nWt72l937ntzewKJ5Ge3RKJhHz605/GMcccg4mJCVx++eUAgM2bN+Pkk0/Gs571LKxcuRJFUQAABoMBVq5ciYmJCZx88sn47ne/u/sHHJarXbNmv1ddV1xhRShQT8311wMnnGAFKq/nMN/InieK1EgkEllAkDANB1W0qqiyYGCxVxYJin6GqrQCUCQaWaewuaduscdRNqe1kkPtPnyfVamc2Bz9X4caJH5hcdoWtLzeKLK9UPsZBi7EF7DOaK+Xu4qydXirX18kKMrUV75tFwFKpPbi1PaOLf1iXVs3D267JKuQdQbIu4OR+a3skAphXdA0LdHp9pF37T5Z4AxPJVBtv9S6r6mqhG/B0+6xqlyLl3aIM48hXHg8eV4GYbbWqVRaYKxrW8lwLqpMbN4ot5EJXepBmfiF92fHM8sq5GlpXdO09JWGE6kaFXlHjXWUUM1diHRYIZn35aXXy9HrdTDp2u/MWqAihxBLZ7VEFh6hEGE2bLDrdwWlFC688EKsX78eDz74IG666SY8+OCDuOiii/CmN70JmzZtwsEHH4wPf/jDAIAPf/jDOPjgg/HQQw/hTW96Ey666KJdG8Bs2LDBOqiXXGJ/tidmP4PDfDdssAtX8n344ebULF++36ft7jVEkRqJRCILiDBALBSoYbip7w+qBAa+CJEroiQ0krxEkpdDDqovwOR7rSovUMNQ37KXjxSqqkgaItSuSxs5rdZNJR/mW/Ry9LZ3fdVbFieqqvuEai0w2cvR62cuhDXBZC/34Z+8LQufzInSNKuQZlVDCClle5Oywyql8m5gmpUj81G5nykfP8kqG+or6jxX5Rzi9rxw/im5JRSqLFCnKwjkxR4XXHJj9OKZw3X54QS7rU4YJu76psMK/dFLmK/KIpcFKotQXtokshaqjTkJQnzDHNZwKcrU3+uisBWEpyv8NBUGA+jqO7NaIguPUIgAdfjmsmW7dtx77rkHExMTWLp0KbIsw6pVq3DHHXfgrrvuwjnnnAMAOO+883D77bcDAO644w6cd955AIBzzjkHX/jCF7Bb2+WGcauXXlrHt+7HQjWs5Pvyl9t2bv/0T8Btt+33U7PXEkVqJBKJLCD4zxwO4y0H1hmsXC/LsrRuIfeYBGAdv06BNC9tSOggRTVIh6r7AraVTVghl0Wl8f0wnahwjiovil1KF9bL4by81PmnqS2U1Mt8mC/nH/Z7HQz61i0dlNYpLfhnkfjQ1Larlucl8s4AeW5dzjD/tA1X+GWqIrWhxv18qJ8o0y6OxKHDABphvqPa3/h5dM5pCIdmc79WPhZXus07AxeO3G8I8DSzubBKSR/qzIIwyyof2stFlLy76oQx55XW8ylQVXYB4HJYNfK0qvvRom5vwwKVaTwEaBRMaoVXJ8OuKTvGaVa7wSxQ+T4nUvvQ4hnjclJns0QWHmG+4TveMXf5hY8++iiOPPJI/3rJkiV49NFHcdBBByFJksa69vZJkuDAAw/EE088sWuDmAkbNzYvPMaxArDTsGwZMDkJ/NEf1cWR4tTsncTCSZFIJLKAMJqsMK0E+Dmj1sILHW6vwk6bDWe1ApVdv6pIGhV8GW4/M6q/Z2M713pG96R3W60QDcN6hf+pKwkdtMHRlfT5slWRWKFWSfR6eUOQclVazpOsj20ry3LYaZqVXqiyy9lGKRm4ns1e36jeAAAgAElEQVTKw1WRoN/r+Ne2bU2z/2p43HZob9mac4aLSnE7Fz/+4H6xsGXByXmu3NKG5LBbTq5FS1gBWSbWZW0XKgKsUxoWQ6paYrqqhsV1O4eVBWIYthvmw4YVklUloSAbDqoQGpgiZFcmyhelUpBDIdCh8J4VMb90vyJMxQxbiuwKo1xQpYajB7gY0qjtd2vxp1HxqrFcLTZsAP7t3+oI6FjJd+8mitTIXsO/3303iq1bd7hddtBB+KUXvnA3jCgS2RshXyCpLXRKl29ZCx3thRX3QNWafFivz0cN8lJ9+GggRrSS/rUP+1VW3AkpIKRGVSQNZ5bFaeUKOIVwFWKu4loXykmtg1om6A8SdPLKt3MBmq1SmCwtkaVWQHJ+KDDs2vEc+DEAfu76vQ7KIh0Zsiqk9m1j7JwqL/LYPfWtbFTTpQ1piMfggULYCqfT7fv9WZy2ha/wLqkNQe71cl8FOJEaZavCr9ICk70MSgvk7V5E7n3lHiBIYWx1ZNczNaz4G7qno+apfa2232pz20QqKCHsT+7VqwVk0HNVu1xYf7072YN1KggA6WiL7k+0UzHnQoAsWbIEjzzyiH+9ZcsWPOMZz8DWrVtRVRWSJMGWLVtw+OGHN7ZfsmQJqqrCT3/6UxxyyCG7NojIrOC+qECzC8/jj8dKvns7UaRG9hqKrVvx/EMP3eF2X5+vvmKRyALAaLjKvakXSOxalmXqQj6taAldOBteOfpJ/lBuakughj8FuM+paIpS55ZqJ0i1C/ssB2lDpLZd323bxjDoWwd1UKbo9VNs76XIM+XFEoCROZLsosqW+1uWKbSur4Hnys9hIOwBNCr7KiUhlIZWVnxnLgc0DOstitQ7naX7HQiczkBgqincQ6WEv291rmktUHnf9v7hg4nSFZDivrcs9NmJHJQpikJiey9DJ7fVeCslEWhC9Aej/wwQ0jQKLrFATbNyqC0Qu6gc6utb4JTpUF9UblfTuKZKonC/DwZ1kaSwv2siFfLOYORYp8WgjpGP7PO0W4gsXz43QmTZsmXYtGkTNm/ejCOOOAJr167FjTfeiPvuuw+33HILVq1ahWuvvRZnnnkmAOCMM87Atddei1NOOQW33HILXvziF++TbXQWApyn/MpX1gKVPxOrVtkw3yhS906iSI1EIpEFhDG2GBKLU26PwqRZHdY7CiEMEue8VYO04Z6yMCNpfA4qrwesq1iLVit6RfB+5cJey0Hq8y9ZUANoiOrS5ZoO+jkme5yHmmB7z26bp1XDzUugh4RqlpY+FxNo9mwNQ1rZsWS4d2pIp9v3TnQoHrkAEwq738Dl0CJDw7VmWGCGVZZHFpkKxGfHVSHmdby/EBplmfpras/hYGAFauHmjHNPK2ULDfF8VorQyW0IrxTah0+zi5pnqhHya/NRy6E+rdyCh/vPokhRcNujohaXoUjmkGyuAhyGJwOALoQXs5WSXljzPebCT8L1Y50NsZ3M/sN0qZi7IkSSJMFVV12F0047DUopnH/++Tj22GPxnve8B6tWrcKf//mf48QTT8QFF1wAALjggguwevVqTExM4JBDDsHatWvn4Op2AFuG4YVu2GAvfj8uVxvmKT/tacMtY6NA3XuJIjUSiUQWEFpTLaaCwjOcHxi2TAHQCFUFrGsaitjQgRRSQ7pc1bZQBYbzTAHUPzVBVwJVaQsRMVWRDI23KJOhCq7b+xkGhcSgFDjkgEGjN2elpP0dGoPS/re1uNMfWVWWw3H7vdxfG7uNoTDUUjTCopUT6AznoYY5l5UTuyzK2vtPFerbhisM83iToBWLUtJVvdVQld0udILDuaxzQAUGZdrI3R2UiReeeaobOZ1CGkDBPxAAgCTRqKq65QyHS7cLJIWvZaIglMCgn/sCTQyLTWRAFoQZT1XQKnTSgaBYkqtS3G5XMyOiSN1vmM9UzBUrVmDFihWNdUuXLsU999wztG2n08HNN9+86yedCWwZsgILbeX9nPnIU47MP1GkRhYc3/n2t3d625i/GtnXMIa8u6Yq6UMuMwAia/413haoupJAVvm80iQr6zBeqbxA1SNCVL1A5bDequngclhvWaYYOJHK4nAwsOMNc1DZ8SsK6QWVFAZjuQ3zZYEawgIsy5QPGw2FOTuohRNyLEp5nO1WLyLIdw2d6U63P7IAE0kNITWEqtvV1O6nHgqvDh3eqkgaDq5s5bo25jootBQKa74mFvu+j6wWDXeUq/UCwHi3RJ7ag/X6GQAnYJVApQhJmHeaaJ+TOopQGAN1/1oWmEBdWEkr8o7sqF6qunBi2u3L47ciWTVCjTnEeLpWPVMSK/VG9hdCy3DNmmHLcBe54qtXYNnhy7D86Pp4GzZvwMbHNuJtv7x3ObVtU5n7or7kJXOXpxyZf6JIjSw49PbtO5W7CsT81ci+hzHkxclQiw9uD+PcuHREeKSQus45lQAp65jKrEI6NrACdTBaqFYudFdXLq9U170+yzL1eabcUqYok0YYKADvnLYdv8Q5d3laIctcf89ALBVBXmvOPUpbziXnirLbqSrZqEDbLqAUOsM8b+ygtsUj78v5vllWWlHqkinbeaRSKt9fFQAqJENhxOyYAmi4kFPlsYYhy0WZNO49C1TOMU0SjfFO0cgt3frk2FAOqhRhcST7cGCqVi9eGLtD2J62OZ7c3qnzVFF/Nse6BbrdZh4p56xyaDKHeXMYd7dTYqw7aIQHj+q1utNQDkp/fnb74t9nuV8ksoeYB8uQxemyw5fh3FvOxbpzrDO79ptr8fH/+Lh/vTcRmsoAcPbZti/q299uX8eCSQuDKFIjkUhkAWFMsx0Lw70zpRNLuctzBMKQ3OH9hFRQKvEuoJAKWgrfLkS7Aj+2Um/q801L5wy2q9Syc1oWKbZt7w71NGXn1IsaJ1BHVe5tY0NAle8jym5mSFmkvqUNz1PbYZ4K7kMKoBFyq6raFa2CUF3AutWmtQ5AI780hAsutZGJhoSGqIJWLiNcVKB2MP2+QjcEaievvHvKOZ31duSFaeKLEmnvYLJAnMq15DktysS2DCrrcXC7IMC63aMcWRaoHA4cPqTI08o7ryxKU1Gi2x2MdJx3CtOHKR6a3b6RyEJjHkobh+J03TnrsOIfV0AbjW7axW0rb8Pyo5fvdY5qaCo/97lWoN5+ez0Vc5GnHJl/okiNRCKRBUZYURUAxsd7vn0JSY2sUyAbG/jWMmAXqgKKXubFK+edaiVRbOtCFanPQ9VBASFuJaMr4UN6w0I+XAiJ8017vQ4me7kP4Wy3M+GiSMBwP057btsH1VeJdYV0hDQY6w4aFWbrfYK2ME6U8zZh/04AQz08ubpv2DuWQ25HOZ1taITo9PMTrOsEriK73UzinMMkBaoyRemcY67k6++FFijKxIdQy0RjUKZ2np3Y73ZKP49Fq/3PeLf04cBSGC9os6zyc8ufrbAPK2AFptYCpdC+XRAAjHULfx8HSF2or6rnMUBVAr1+hkoJn1/MY+h2iqHiSPxAYtYiFbFwUmQ/YQ5LG4ehvcuPXo5156zD2R87GycdfhK00Sh0gZXHrPQCNXRY9xamM5VjuO/CIIrUSCQSWUAQmYZA7XYHGF806avDdroDdBdPQgjjW8sIODc1UdCVROGKCtlqvXXIK7eKCav9Aq4okrZVhYt+3igSpJT0lXrLIrUhoP3MCxCg2d80LIgEAMJVomV8BV9X7WZQJj5HlV02Dv+UUjWuQammw8iooFUOO4HeqQvCe9thwX7OhYZM4PMo27QFVCjg+cztEOKqSKGlgIRGkpYQQZsXwQ5uqw8rAD/PgOtxWghboAh1qPSYE8O2arIE3H3NMgVAue3tOp7bse4AeWfgc31HtpQpEhRuv0pJX7k37KEKAEoKZE7w83b+PS18eG845jwtG61uAPvZzjuDOrR6NhjEwkmR/YM5KG08XWhvoQp8YfMXMJaOYeVxK3HDAzfgWz/8Fh76yUO4feXtPld1T7qqYS4qm8qrVwNXXhmF6UIkitRIJBJZQBCZhts0Nj7p8xuF0N6Rm45iMreVa7kNiBNxRT+zx3ACQzhRa7exbmm/l6MsU4yNT9ZC1YWfcn7ioEx8uGmb0FHdEZUS3uXjNibtli9MKORYgI4Sp6GrmnaHw3sBQMJV1h2Rm0lSQ2K0ewq49jFpaYtbORc0DO9tt8tRlUCSjq56a4IHCGFF30pJX3gK4IcAw/OdSAUth3szJlJDae1/ZxE5KuQ6bCvDrW34IYMUuhGaCwCLFk+iLNLG9YSin/ORbVGlylfwlYlGu1BWyC45qbFwUmR/YA5KG7dDe8/+2NkoVAFBAoIExtIxJCLBa094LQDg+geuRy7rau572lXlXNSLLwYuu6z+eemlMQ91ITKLUnmRSCQS2VMQgDQtrevVGSDvDrzAYnFZtdzEsCKvLX40vPS3dVD08sa6YjJH0c9QTOaoXIivkBppWuLJny7G9m1j+NlPF6HXy334p9IicPQKZJlqhfqWyNMSlZJe9LB7GubaWnevwKJFfZ8nOUqghsWIAPhw1bDYDheYCgsncU4rFzwCrGPKC8+l4hBnVzRqELTXCQtVsUhOswpJVrnep5UXqO1eqaHoUqp5X3pPdhuOtQgeBgDwApFFnQ3VLbzI5Dxafk9I4/NF2bW2+Z/2PSk0BoPUF0LipShTfyyuIBwKVA7FzdLKL0Jo5J2Bn980s59VAD5EOU8rLB7vo9spfLubqQSqdkK/KtKhz/VOo2e5RCIBn/70p3HMMcdgYmICl19+eeO9N7zhDVi0aJF/fc011+DQQw/FCSecgBNOOAEf+tCHdvdwd5orvnoFNmzeAACN0N53f/ndKFSBXtWDgcGrf+nV+OSrPonbV96Osz52Fu78zzux+vjVIBBW/OMKvOa213iBymHAV3z1it16LWweX3IJcPrpVqCuWwe8+c21qRxZOESRGolEIgsIEgadrhWnY4smAdi8RHLVfEWife6orqQXqAB8r9CqTBvvV0WCQS/3IabesSsS21rG5UiqltDq9XIM+nnQ71QiT61gGusOkGW2EA4LkDwtked2SaRqtE0JF25dcuCB23DgAdswPt7zeai8PzBcBdcWJaqXRgiqq0zLVYE5zNfvyy6qVEhzmwOZuBBUH+rsqgeHwhaoBagQGpm7N0laojveQ9YZ1K7pFFV7jRZe6HJItR2LbpyrCs7D5KmthjvWHaDbsaWGlRPjvH6sO2iIQBat7TY/RWlDiQf9HL1exwvd0LXN09ILS55L7lcrE4Vud+CXPC/978IVd+Kqv2PdARYtnkS320e322+EWof9f3nelBOrM8YQoGe5RBYeV1xh4zxDNmyw63cBpRQuvPBCrF+/Hg8++CBuuukmPPjggwCAe++9F1u3bh3aZ+XKlbj//vtx//3343Wve90unX8+YfeUhSoAH9pLRHjJ0S9BIhKsOm6VD+klEFYeuxLXnX0dPvXqT0EbjesfuB6nT5yOjY9txJVfuxLn3nIulh2+DAB2q2Bdvhx4y1uA66+3Oalh9PMoszmy9xLDfSORSGQBQaR9eO90fSPtH/v1H9pWkDrx2tqvt71r26+kJYwWvsVMuO0ogbV9+5j/XTrBMlU473ShnEAtwA4+YBJZVjnx0mxfMqoNyVRVdEO4ANABB27zfVXzoGLsqFDbxthc31XbOqbZP1UIDaRW5E4Xaq1ciGtjP36vEpCJ/VkG1XLDkGIplRfng0HaaOXit0k0ctT9RWdKmHPaJpEKWbfyglJrgSyt/D3heQ0/J+E1ysS6r4vH7X1tF78qgEbEMrcOErPNRXUQYrjvfkXYe4QTE8NeJLPknnvuwcTEBJYuXQoAWLVqFe644w4cc8wxeOtb34obb7wRt91221xcwcxoNwQF7DVv3LhDRRYWR1p3zjqce8u5OH3idNz8rZshhfShvW//Fdu3hV3SjY9t9FV9mW7axbEHH4sbHrgBL136Unz+4c/jvS977x4prDQPBY4je4goUiORSGQBIYRByg6g+1EWCdKsgq5qQRmKhVB0MmXwe1WkjRxJIB2qZms4L9KJSRaVdT6j/WkrzUrfPxSAzzm0+9XjyrLKF/UBgG6nxCLnmrarvIb9Tv116bo1TCMnNags2+vl0Fr48GhuARMK1FDQhXNXFQmKfu2g5t1BPfcOLnrkx1QJIGmOpx3qOwpVCd9XdSq5naa2zZCfB+e4hnMazhsXLVKV8NV3KyWQte6ZCh5a8LE4LJjd1iyrMD4+6dvBsOPOYwlDsVnQA2iI7jA8uB26XSnRqLqcBTnNU7Xt2Wli6O7+Q9h7ZM0aq1LmIBHx0UcfxZFHHulfL1myBP/6r/+Kq666CmeccQYOO+ywoX1uvfVWfOlLX8Kzn/1svO9972vsP2fsgigP80+XH70cp0+c7nNMX/1Lr8aq41YBQCNHtV0QiQUoi9bX3PYaXP/A9Th16am4ZMMluP8H92P9Q+sbIcBzXVSpXSyJc1Krqv4oxFzUhUkUqZFIJLKQIOMdOxLSOp+FzRsk0XQ7S5cDaLRoiM5RuX2pC20FhtutKCV9L1TACg+tRaOqq9YCeW6PYYvjSJ+7GOYcsvvHoinLKlfhV+OARZNYtHjSu3J87lAMtWm/F7rEg0GKokyRpTbslN3TpsNX/26CfVUlvEDNshJJVtp805Zb2mj1A/i80lHjCWk7nTznnE8bOthSaignrFms+RzbohaWXddKx1M0Bawfk8stnQqtyFdVhgKSrsL4+CQWLZpEp2vDl8P+uEJqX4BKKdF4QCGE9oWUxsd7kIlC6vKLQyFfFil06MomYei2Hgqx3mkoB7KJme8HAPj3We4X2aNM13tklhgz/O9lMBjg5ptvxhe/+MWh917xilfgVa96FfI8xwc+8AGcd955uOuuu3Z5HEPsgihvO6g3PHADnvf05+GhnzzUCO0NxWnongLAxsc2NgTo+ofWY/Xxq/Hxb38cv3zkL+P6B67H6uNXz6ujGur0jRvrYkk8DbEn6sIlitRIJBJZwLC4KcvUCx9yIoxFl80nlYHws64ou6fdRT3bZsWHa4qG+AtFV9iHtOGGVfDCNemqWoy2iuIMhf06wyzrVhgf740Uo6NCSevrl43/ycLXZZH68NRRAmdUSCw7mjbEV3iBOhXc2icUquE42XklF07dbkXD28sEowr0emc1y8qhnEwWtPAVeOXIkOjGuXQz7BhAUCWY3fC6ZU0iFRaN93yIbnts4c/G2IJz6ER4J3xUz1MdXBeHEKc7UaV6p9ADoP/Q3BwrsjCYh3jPJUuW4JFHHvGvt2zZgqOOOgqf+MQnMDFhH4JMTk5iYmICDz30EJ7ylKf4bX/v934PF1100S6df1pmKMrbPVDZQT3x6Sfi63/w9YaY5G3a4pRhR7S9zwlPPwF/8tk/walLT8UND9wAAA1Hda4vfzqdHsN9Fy7zJlKJ6CMAXg7gh8aY49y6vwbwCtjnrN8B8FpjzFb33sUALoDtff5GY8xn3PrfAPB3ACSADxljLm+fKxKJRPYfyIsgo2tBRVIHDqhoOHjsnI6SL1IqZK6wDQAIrb07W7qKvhzmy31IKzXsbCrUwlUIDeH6gI7qPdoWqrksMT7eQ6fb931P+Rq1Ft55Q1E7q0Cdm4kyabiPwokeDhvmMRhXQZhchWJ2RasgJJWLS9m50cg6tkpvVSRDbrWUyp6zApDUIcZ8b5isU6AqEsjEFbdqzZ3QttgVO+Ohk51mFUSlUSLFsEQP5r8S0FIMCV12tdkdBVyotgIKd8Q63Df17WEWj9uqymlmXehOtz8UcmvDpnWzHY9zVkMhmsK6pmlWInd5xu0HH/yZkonyvVELlwsM2D8AqtkUTgJsr9TI/kEY7srqZA7iPZctW4ZNmzZh8+bNOOKII7B27VrceOONePvb3+63WbRoER56yD4Q+f73v+9DgO+880485znP2bXrmo4ZivJ2D9Sbv3UzMpHh4Z88jA2bN3iHdeNjG3daULYd1cu+chne+7L3otIVnr7o6Q1HdT6YB/M8shcwn07qNQCuAnBdsO5zAC42xlRE9B4AFwO4iIh+EcAqAMcCOBzA54no2W6fvwdwKoAtADYS0Z3GmAfncdyRSCSy92KaDmkYusvuZyhcG7v6di21aOsesA2ZqwqrNQGVzbPkSr+NMF8nUIFm7qOqpA3VrGonLJEaRRn8F+PEU+j0qUoiARoClUM8WZBy8RwpNZCVUL3aNeQqsCwM2RHkdjWLXPVj38KFrx8AUiDJKmgtQMoK8/Z8Jq4CMFf7HfRyGC286wzUOah8fZwXzNuIoGqwSLjoVa2aeM75f2MX8ArAClTbDsceo0Q6ZX4rV+BNUBcsqpT0fUnDvNNwn/4ggdKEShHyVCNJNLodWzE479gKvXln4B1gpSSME/qj4M9WW9AKqW1Ob+CQqp59+MGFrbLUFqVi95qkRm9bd5d6pAIAxUq9+w8bNw7baHMQ75kkCa666iqcdtppUErh/PPPx7HHHjvl9u9///tx5513IkkSHHLIIbjmmmtmfe5pmYUoZxF61sfOQqUrZEmG21feDgBDDurOEuaYtgXrX/7zX+LUpafi49/+OF57wmvnJTc1FkvaN5k3kWqM+RIRHdVa99ng5d0AznG/nwlgrTFmAGAzET0E4AXuvYeMMQ8DABGtddtGkRqJRPZ7hKhdLM61lIlG2bOhqqHz1s7py9y2nUWtPEbUbmVVJI08VABDoaKAFZ4ZgCpwCNvtUkQ2uhox99Rsn78Wlk6UBu6qqiSKMnE/eVzKO7QysZVwR1WGlVL78N1wPCzKVSDkw7DdqdrHMN7FTbQXpkJoCBcKrDUhySo/pmowuudnOy82ybR3zP25WkLVt4pxP/k/9qJIMCiToMiV9rnAANAfJBiU7qFDqtHJK3Q7to8tu9BpagW6UQLgfrKymTMcQlIjD+8nu/hO3MJd9s4UkzLeld3FykfRSd1/GFXRdo4Uy4oVK7BixYop39+2bZv//bLLLsNll122y+fcITspysMQX8AK1YmDJ3DfD+7DJb96yVD+6a44nu0Q4Hctfxcu+8pluHT5pTj3lnNx8YsuxmVfuWyXc1O5YBLQrBX1+OOxWNK+wp7MST0fwMfc70fAilZmi1sHAI+01p88/0OLzBX/fvfdKEb0DxvF5m9/G88/9NB5HlEkssChOudUJgBQegeV+35KqVAhGWphwo4pu3qj8j+58E8VhFryzzQrGz0sG/snVhwORogvL2xb7iqHkobnCMcrpYJQtvCOlmJoGxZuWVo2WqHwz0YxH9QClfNEubBU2GrHilPhK/oWMvMhuOH8MqEgDWFxmgRiWQT7aSVsPqu7Dl0JVGXqc2IB+2CAHw4Y1ye0LOw6dpD9uIVGr581ep9O9rKRAjWRuhE6O96tMN4pfJ9VDpEOW8AoJUBT9XkNnNWwAJfHfZaUqlvsKCW82PYPOdz7fE6+B7uEQex5Gtl32UlR3q7ke+XXrsQ3fvANrD5+Na6+92osP2r5DvNPZ0roqJ749BN9gaZ3bHgHPvGqT+zyebhg0itfWQtUFqerVsViSfsCe0SkEtHbYQOc/pFXjdjMgGOehtePOubvA/h9AHjGM54xB6OMzAXF1q07LTw33XvvPI8mEtkXMLWLBfiCOzJpOaqBw2rzAatGyClg29kIzqWsJHRl26+UZerDfDkPNc2su9Z28fgcoZjwuaJw4qjVPqZwOaRcjIfPE4rh0D1rC1SZKKhe7isIc89NPo/WAqkLF+Ux8DjDCr1tUQi43qRBeLN0LV/aBY/abqlf7+ZUSO0WBZIG0s05O4iiSK04dv1rlSs01XabuYouYIVtv9fxAlNV0oc48/3v9VNIlzc7KIf/i0+COUkSuyweH2CsO0CWco9V60SH1XV5LKOq7LJrz+HRIY1+sEpCKt1wxXkbzmlWlf28MXzuqcKLd4ropEb2U0b1Qj3x6Sficw9/Dn/zsr/Bm09581DRo7kiDOVdfvRyrDlpDd71pXc1nNtdISyY9LSnjS6YFFnYzLIKwewhovNgCyq92tQ1vbcACBtILQHw2DTrhzDGfNAYc5Ix5qRDoxsXiUT2UUgYJFk1JJpUJWC0dePKMvXFgdqVbYd6jTpxqjUFLqoVN+0COFPRdjgTOdqlBZxrllZeoEonMr1zOqI4D4cDt8Uwt8Dh/pscmsp5jfUx1MjrmEoUch/V1OVGJg2BXyEbGyDJSmSdwt+LrFvYZWyArDtAvqiHbFEPMhsu2avDHrauUBOH9HoH01c0rh1HLjDEc2x7jpb+WipXGEpp4av48sKEDqotkDTAAYsm0e32bVVd18c0dFEBKxJDochj5IrF/OCDw5WNFiPDlLkYUnhNQ/OjhReqyjnIxi2zwsxyiUQWOOygckGk0ydOx+ce/hxOXXoq3nzKmwGgUShpvtiweQOuvvdqXPKrl+Dqe6/Ghs0b5uS4YcGkNWuiMN3X2K1OqqvUexGAXzPGTAZv3QngRiK6ErZw0rMA3APrsD6LiI4G8ChscaXf2Z1jjkQikb0JIuMK/hCEFoDTYkU/t44WO2VZEHbpdFLt/tm/wFmMai1QDqyzV/RzAM2QVpnYKrbe8axkI4+0LfRkorwYqpTwPS8ZkWlfLZZ7bipX1XdkqxgnGn3YrLKuKSp4cTpKULPzxwWIin42sp/sKDg0OOsUviKtndfKO6VAXZRJZpUP55V5hXTM7lNO5sCgdlC1klBFgmqQehcVaPamDe8bF4wCrKPMjrGf+0RBJnVl5zytvChtV8MNiycpLbB4vI9F4z3vRNtzN9u/hD1KwwrKjXGi3r4s6krLSklbFVk13VGgWaXZOsJhLq5tIzRwn0Vmqgcf0xLDfSP7MaN6oa4+fjXWP7TeC1febr4q74ZO7cbHNuLiF13ccG53pYhSLJi0bzOfLWhuAvDrAJ5KRFsA/AVsNd8cwOeICADuNvEXTyUAACAASURBVMa83hjzLSJaB1sQqQJwoTFGueP8IYDPwBZk/Igx5lvzNeZIJBLZ6yGb74hKNtq79Hs5lJLodPu2iqoTZoANCebfw9BUwOWglqmvbBsKXSYUHaOKH7V7lQIu7zHYPyTNmv1CFYZ7bYauHRfQYVgwIYF3UcNWKKHjFraaSbPKO85TERaiSrMKeXdQPxQIckw5jBewuaYyr0VqMlZAdAIRXwmoIoVRBO2EW+hYM1z4aipEIFD5+oUSyNISW3uLrIMtawswkwpaWYHWEIFOoIYtZoC6OnQ4z+F8y0T7frjhOMPfw9ZFYci4P3dLnPLY2ucOhSvnQe+oB+zoSesA3YmZ7wcAuH+W+0Ui8wRXCwqV2IYNNgFzVH4q0OiFuvr41bju7OvmLcR3FGFuKgBfPImd27AdzkyYp25Dkb2I+azu+6oRqz88zfbvBvDuEes/BeBTczi0SCQSWbgY+PBc7Qr6cA/KFKUXqElaeue0DIoZ6Ur6np5VkWDQyxuigpEtUahboZtlkdrcUFdpV6hhlw9AI6wXAFCMDr9tns+GkYY9S9skUkPmJcbGJ4cKGikEzqUOqwI3XUuZaEholM7t42OwIGNxq50Txw4qu6YsaEWikY4N/GuaRkxpZasmi0RBuIJJ/j0tIDG1SA3zgcMCVgBwwKJJX7SK29EAgJDGC1WGBSo/fBj14CGsqsutgPz8KQlRTT1O/ixxaxkAjQcqQC1CR52bxSnvz7nHcmSn3x2g+8Dkd2a+XySyN8LVgliJhUrN0a7ku2HzBtz8rZtx4tNPbDioc1HJd2do56ays7vmpDWzEsqs08PCxqzT56DbUGQvYrfnpEYikUhk9hhjc0eLyRy9J7v+j/y8O0B3vGfzJJ1ATVxOZSgIbSVZiapIff6qDcm04bztwkiSCwA5kcHioigT9Hq5L4IUig8WT5w3ycV3OL80ySqkbhlF5vpycrGnUUVzZKIa/TtZXHIuJ+dQKiVR9DMU/QxlWYsmFqFJ2sxdZYFKom4lA7iCSE6gyqyEzCskeYkkL+3r8QHkmF1EVsE4QaerZi5lOL+cxxk6qqoSfrECuy5e5SvjujkORR47onleQgrtW/Ik7YcBQmPReM9dp31vMEgxGKQN0RvOu3FVd7UW/vPClYd5TFPB9yQUw+F5wu20tp8rzkkNBWqalbML9wVAenZLJBLy6U9/GscccwwmJiZw+eWXAwDuuusuPO95z8Nxxx2H8847D1Vlv9OMMXjjG9+IiYkJHH/88bjvvvvmZhBhtaB3vGOkdRjmoW7YvAFnf+xsZEmGv3nZ33iByEJ1rvqUzugSgiJKa05aM2ORzDqdDWXW6fx6CkM5sgCJIjUSiUQWEEYTBr0cg17uXcI0LX1oapqXvr2MEMZVm20VS3I5qGUQcjrTnqChGGXaOZBpZgsYjXJJffixaL6XZBVkopHmVkDy+3WlWetmcnGkUJxOhc2NDFziQISKxOas+iJT7r2U27C4VjJ1xV5bQImFqswrpAf2IPIKIq9AqQaluiFUQ6zgN35pvDeFs6mD1i12/C6sWGpfhCpLKy/kWKjW5wxCgDNlc4ST1oMLLRqil/OAQ6FauYrEpfvJS9HKHZ3yPgSfGT5XmKsaPggpShbOzUJPM8aQzUmdzRJZcFzx1SuGivJs2LwBV3z1il06rlIKF154IdavX48HH3wQN910Ex588EGcd955WLt2Lb75zW/imc98Jq699loAwPr167Fp0yZs2rQJH/zgB7FmzZpdOn+DHVQLCt3Kd3/53TAwuH3l7T7vdL6LJO2IXS2itBM6PbKPEEVqJBKJLCSckxriHSolW+Gj5ENVAevq6coKVOMEbtiqhtumcFVZe0znsDphyn1O20KH4fVcLEm1hGu7CqzWwhc44orEyo2T8xe54m67qjG7jKNCRkP3mJ3MsAUNv69bQpJEXSQoyaqGQJVZ1QjxFbl1UEU+7Aj7Krfu2oRUkK4KbpLX7m34AGHUdSRZ1XCyASvmOt0+xhZNYmzR5NA+Q8eQqtEnNTwO0BSPyrUhUk6UcuguL+zo9nsdlIX9qUaE7bZDutn55YU/T5UrxMVCmR3eRNqCUGG+a/shyE4Tq/vuN4QuIlAX7Vl2+LJdOu4999yDiYkJLF26FFmWYdWqVbj11luR5zme/exnAwBOPfVU3HrrrQCAO+64A695zWtARHjhC1+IrVu34vvf//6uXRzTrha0YVjksVv5hc1fwB+d/EcNt3JPOahAs4jSpcsvbTi7MyFW9d0/2CN9UiORSCQyO7hxFxexAeocwSTTtQvq+qcCLuRUC1Q+3NIKAikVIBXgCyZxoRzuZSl839TBIPWuV1gNdlReYbs3Jv/MsrIR4sv5tPacrYJOroepUrJRXTY8rlISKAAjBZIgHDQMP2aBylV+k7Tu5Tno5V5MNvrMJtq1mal8eG6Slb4wEuejiqyCXDQACQM9SGphWgqYSkC7HFGRaJ9pyi1pODfVHrtqPHhou8LcTofnv9Pt121xnKh+8qeL67njORXaV/QV0iBz4+/1Ouh2+/5c3KM0pN/rNCs4B2HHAKAgoRPR6MnbRrhwax08kADgRan9XUBLAYm6nRCL5vAPFFXJkePcKaIrut/Qznm8+t6r56Q40KOPPoojj6w7Ii5ZsgR33303yrLEvffei5NOOgm33HILHnnkkSm3f/TRR3HYYYft0jimqxZ0RbbR56KyW7n6+NW48mtXYvlR81e9dyZwESV2ctvO7s5W+Y1VffcPopMaiUQiCwryIakyqR027fpSskMaOoS2bUwz1xGo8wXJ5YvyAtSFjLQSXqBqLeq8R9dTk3uissDYUd4gCe1DfYFmJd6wuq2q6iqxYa6oTHTDpSudwG6HKLfHwdvbEF8F4ZxeHg8vItG+/6mQyuf1kjRWmCYayVhh808XDUB5BeqWNtRXaJhSQBcJjGsvQ0GIsEi0c4xN3cJmmj62YX6tFfhlndPrQry9u+zaBAHwLiQA756GOaqTvXwoVHtU+G/pqvOyUC+L1DuhRZn4VkQU5iy37kP74QOH8lZKejHN8GdpKpd+1hia3RJZkOxqzuMojBm21oUQWLt2Ld70pjfhBS94ARYvXowkSabc3nW12DXCakFAHfu6caN3ka/82pW+gu76h9bj0uWXzsqtnA/e9stvw/Kjlw/1b+XXUzneV1xRG8as0y++GFi0qA79HWEoRxY40UmNRCKRBYQxVsDJJKhS2+4PqoWtg1pYURa2XJmqYm67fYtSwod2JlJDJwrddODdtTB3Mqzq60M2Xfgw9xiVyjmvQW9WDiuVsnaAdVGPj0NlGTUixzMUbQwLNK3VUIiorgR00Cc1DUJvAfhwXBHMKYf5ikQjW9xDcmAPslvYN0vZiAylVEOggkmC+Uw09KSEGiTQSsIo8n1SuY/oqFDf8F5xmLRSNvxZSwFd2LmuisSLzlCo+nkr6mOzMNy2vYsDXXXmUYKQRWdZphBCoyxS9Hp5owVOp9u34jkth/b351Z1kScWqOF95BZCWVrV1zyisBIXUZoxBpimYPIuQUQfAfByAD80xhzXeu9PAPw1gEONMf8zPyPYs0x3/e59AvB3AFYAmATwu8aYOaogNDXtnMe5cBGXLFniXVIA2LJlCw4//HCccsop+PKXvwwA+OxnP4v/+q//mnb7XWZUVSBnIy4HsO6cdXj5TS/Hbz3nt3DZVy7zLvKJTz9xt1Ty3Vlm6niHRY03brQC9bLLar0eq/rum0QnNRKJRBYUdesZJiwcxC4lO6plUHxG67r4TeX6olZF6p3JNC29GLIOZS0Kxsd7GFs06d08X9DIOappWiLvDBpOIAsYPi5JjaKfo+jnfhwAgvxX6debafIPwxDhJKvQcb1M2Wltu7UsdFUlUJX19ducU91Ysm7h5wCwAlXmlW8zkxzYgzygB1o0AB3cA3WbAo2ELZxEQkN0SojcLgCgigRlL0P/yTE/BqWkf4gwVZ9Udk2zzsCHYvO9623vYnL7mBeB4eeBhWqYi6oV+fW9XgdaC2Rp1QiR5ocMfJ/Dqrt8Dm4tFM5VfX+cm6+aBZl8yyQt6jBk0XRPQ0ELWFE9GFjnNZtGDE+HMTSrZSe4BsBvtFcS0ZEATgXwvVkNeOFwDUZcf8DpAJ7llt8HcPV8D2iuch7bLFu2DJs2bcLmzZtRFAXWrl2LM844Az/84Q8BAIPBAO95z3vw+te/HgBwxhln4LrrroMxBnfffTcOPPDAXQ/13QmWH70cbznlLbj+gesbLvKezEOdipk43mGxpG3bmgKV349Vffc9opMaiUQiC4pmGJlM9JDDyCG97RYv7FyygFCB09nM96wdPCE0RGZdsxAtw0qwyhdc0pJzIF0YMYsuN5Qq6Mc6qrhR/Z72uaj+nMH5jLQua6c78MWOFKw4532yTuH3bQvAbGyANtxmhoJquDKvfA9UOTawAjXRQGrHZ6QGRji8gHtgkMBX+S16uX84oJ1g5nvH98qGccP3bpUudJfXk9SQwT0qC5svPLLXqdD1f/KV7Z+aZZUXrUWRIOnWlYKLKQpQ9Vx4cKUkMqF9JWEW+aM+g1yRmPNPeZzcwzVPbcscWyRJN0QyYB9+6EJ4QZ1IhW53+J7tEJEDYz8/8/0AANNXQDXGfImIjhrx1vsAvA3AHbM88YJgmutnzgRwnbGxr3cT0UFEdJgxZo4qCA3DOY+hOJuLfqBJkuCqq67CaaedBqUUzj//fBx77LF461vfik9+8pPQWmPNmjV48YtfDABYsWIFPvWpT2FiYgJjY2P46Ec/OifXtyPmw0WeL2Y61rBY0iWXRNd0fyCK1EgkEllAEJmmoAxyTK0QFN4xDbEuZVKLT5fnyIQVgsMCSaPgokUsLMoyRYp623C/tuMLDIcn22OKWpQ5gcrnklI1XD7A9lJtu45Gi7q1jXsvzSoXHl23luks6kEHIaWNCshKQCj7mnuiJot7oFTXAhUA+okVqErYkN/gGCQ0DFyOcCVgKolyMoeupFuEzxPma8i6A59HXOnmnIXh3VxtNxSB9XW4vqKtuf1/7L17lGVVfS76zcdaa++qah6idIIYu69ykIePNrTGF9cdokOMkYdagvdyjNHjuD2Ol4wQ0yccT+MRDiG3D5Bhbu7thHvjMGIOWIcERAUPmGwOwWBSnIAIEi4kYEC0lUcr3bVrrTUf9485f3PNtfauprvsbqqK+Y2xx6699tprzbWqxuj+9vf9vi9Yf/0XBYVXdsk+G1uileYQcVp0tBY366pD9y2dk45LM8RdNGqpC/sSfn45Pif83xyprpwbFL3SfdkCZwded/izY8d+TpgS2P3P+/+5ZYIx9l4A37fWfvuAzCCubrwUwGPR68f9tiVJ6oMPPoi3v/3trW2f/vSnwfm+Gf/OePEZQOWOQzgGx+CMF5/R2rYcvOIVr8BXvvKV1lo/9rGP4WMf+1hrG+H888/H+eefP/E9wg9/+MPnrqd57DFg3TrgiCOabbt2Ac8+C7zsZXjsJ49hXeGC0+7/8f046SUn4ca/uBHZngzv/ON34qSXnIQjekcscfDnB7sWd4W1/vl/+XMczg5vrXXX4i48Wz6Llx3ehE/t2gXcfz/w8pc7JfXGG9u3JGHtIZHUhISEhFUExl0arO1YfoGGaCpfF0KI7bQAAhHsWjW1FihHBbQW6PUXQ3hSt0bGTCAiRFSzTi8qEY6wfq8EdtcGOHtwnNLbzK2KVngSKafxumNrMhFULg1y2ZA/Lg16MyMUM04Vrkc5VNkm86rMQh8qEwbZ4SOImRJ8ugJ6Cqg5oB35hOKwpQ9KqZvzs0wDpYRZdKRNlxK6kr4SyCcAR+Qv75fIexWMYVBVBqbHyTx9GRFqYHyIUbhPXumsKgnNeYsEAmgpkvQlAlem1VVK86Zxki6dQ0gNKCDPVPj7cRVDTXpztxppEoQ0yLM6rKGuMp8U3Jw7y+uwfnrd6y9i5vA9z3n8MfxsIUgvZozdFb2+ylp71VI7M8amAHwKwDuXe8I1hkk3fixRiDH2cTg7MIpi3zp3X1BYt86xs5NOcqyM2NpJJ7m3i3W4/8f34yVTL8FJL3HbiAAePX00ni2fXXEk9dny2RZ5vv/H9+Plh78cz5bPhtd0LUD7ko84wj3i1wlrE4mkJiQkJKwqTEqZbJM6IjKEmFRSqmwcSAQ0JMNoHogm7UP2UtoPaAiUEDooelnWEFS2BFHNsho1mrVJYUJAUjeAJ561JcUx9JjK8fXTesbmJHPAKAEuNfJ+GVXJ+KRfICirrhPVdZoWh7mAJFYowN+PcPc9QSVySiouy9oW5nqh8IFJPDqHAdPevhtdI+d2LO03vofK/15Ho2KM/NO8p9JirA+1udftGh8inuFnurSI/Ma1Pv11buaYR38bdO9VZE/eG/KsDhZfADB+DjWupSH7LwBkfUdo+9Mj9KZHez32klh+cNKT1tpT9mP/VwDYCIBU1GMB/ANj7A3W2h8uexWrF48DeFn0+lgAT3R38sT/KgA45ZRT7G233dZ6/4EHHsDxxx9/8Fb5PMIYg+71TgRF2r73va5z5ZZbWn5XmsV97ynvxY67duCW99+yYm2+k0DrpxAlWv/27S40aX7ePQ8G7lbQ6/n5NIu6knCg3SOJpCYkJCSsKrCWiiqjuVNrOFA5RdJo3iKqQEwsx//Xbn3KLjpKKNk3KbAHQJg/BRxByVCPqaVdhNRfaSBkiTruBc3qMetuSC6OlNOgqMr2DCOFD1GFjKuPicOCODC2rbE9c2FghIHR3Kma6xZdSNK6kSOdtQAK1VhZvcWXcQt4UmpL7rpSR7nbZY8np4pDV9IptBEpzIoadZm5/troXqhKulnViPB1v3ygOiCg6YQl6+5UvwxkNLbqdl/H6CY2x/sG623hgrGokkZVElbw8DdBSdAhFbhj3zWGt1TcqvahWaptKc7ypt4IaNT1/rplElTgkNXJWGu/A+Boes0YexTAKWs13XcfcCOATzDGrgXwRgA/OZjzqGsazzGQGYcQbTt126oiqMDS649TfYmgdmtiE9YuEklNSEhIWE2waBHUrKiDnVXDz2BqZ5HUo6aWhHNX8xITNSIjsVW26Jdj25lXO4G9E13e2SYi5c4YN38ps/a8qszVmHXXH601R+pCgBRUJZ2a54N1iOjR+SQF+uxF0TPR9cq8hsgVjBbQlUS+bhH5uhHEVAleRMFTkRptI5s1442ybQ0PFl83g8qhfQpvuEeymQ8loqrqLFy7qiTqOlLBg4VbjqmoLnW3CV+iIKKYKHYVVyD6vXsrbb/vZj9Ho6WtllPTCyj6ZbCE11U8hezs3mXHOk3rd9ZkgX5/Mdh4KYyp8tdKxJpz4xV5HaqF6G+SL7c79SCRVMbYNQDeDmcLfhzAp621f3pQTrYCMen6AWeTsNb+MYCb4OpnHoaroPnI87PSNYDh0Cmo27a55w5DW02BSZOw1PrjVN8tW9ylx6m+CWsbiaQmJCQkrCJYi5DcS+SOSxOIKtCk32Z5jbrKUPRcKqrRPIQlhc9y0yKjccBNTF67s6TNuSL7qJ/j3Jvlk2y6NCsa5kc9kZJQjtBGx4gJCjfcWXJ5EyAVE9m8X0F4khqTUQCwepysMGH9mg1EXrcIamzdBdAQ1XrSnQDMYga1kKNecGSPiC/NvXJuWyFN4bCKQyv3mZighvd9mFVdZajqLMyXKi2wMCoghXapvVKPEdIYVPVCP2uIYLulbXH/Lb2W/v7Q34oQOgQ3EShhuBu6RcFOcsLfBBFs4ZVxmkXlwgSCKnM1Zu3eL1gAE+75gYC19tzneH/DQTnxCsE+XL8F8G8P0XLWLibJh9HruHZnsHGAwYZB6/VKB63/7Fed7chptH4AmM/nsWXL1pTq+wJE6klNSEhIWFVgTlWMFElKi41twEQWe/1FFP0SRb9Eb4kKj5hckto5qa+T+bAl6jwdP44JnZ5ZrlqkR3cIo8xr5FNOISP1kx55r0I+VQaCQqQUwBgJE0Kj6JfheE4ZrSEKl8zLhQ6P8JnOdlnUyKZK9I7cM5GgWsNgR5lL6h1lsIbBlDI89CiH3pMHglqP6JE75Vfq5hEpr0Y52yzdo9p3uJa+qqYcFShHBUa7+1gc9bBnTx9VJVGWjqxWlXSJu5FyGtuBqzoLNmAAE0ls3H9aV+20YJoNVZpjcUT9tjJYe2mWlD5DNTNKN9tj665WrvO0LDN/Lh6ClPr90hFUbkK/Lv0NGjU5sXqfQeFJ+/tISFgJmJ8fKwXdfsXZGH7rWve2r90BgO3f3N6q3VkNoPWfc/I5mL1uFgAw9/45XHvftZi9bhbyR5tbIvLwZ6u8TVhFSEpqQkJCwgEEY+y3AHwMTsP5DpzF7ecBXAvgRQD+AcB51tqKMVYA+AKAXwTwFIAPWmsf3fsZbGsOlRTUbtqvEBpw45GBnGrVVr+6HaacmzDXybQjrSqy+XYJTlxdQkQ3XpvMTSBhMfJeBeltnxTq1LomCu7RHNVC4VRTqQElYIDwmhsGbpxlWHoFTvifw7Gi8zJhHTGVBpAGRgtHZgsFMe3IKS/qloXX3VsGaxiYr5qxtftCwNZNzYzaUwSCqioZQpJaa6GZXm5hAKg6a5Ev7eeItRYhXMoY7ojdYoEFb8fVhkNX/vcS/U6UFgCR0kildMq7q3Hp/t6IOMa/I7IBk+rqSGrPr1FEacLNZ2id8ev4nHRcUlrLMmsRVCKymf8CpJvibNTyv1O341ljCQn7jd/4jd/AV7/6VRx99NG47777AADbtm3Dl7/8ZXDOcfTRR+Pzn/88jjnmGNx2220444wzsHHjRgDA2WefjYsuumh5J56QDLT5bY7QzT1yDra+ZWtLTQXcjOdqUFEBYOtbmuube/8c3nPNe/C+E96Hmx++GRe+cg6XfXyAC68aQh09j7nB1paonLC2kZTUhISEhAMExthLAZwPF5ZyMlww7jkA/g8Af2CtPQ7AMwA+6j/yUQDPWGtfCeAP/H7PcY7xnlFVZ6gjRY7glNTSpc56Gy/N++3NFto6n1dHJ801kqranx6h6JfoT498PUl7fVoL9KdHyL3iSYQSQLB2xkonhRnJXKG3boR8qnSBRlMleutG4fN5v8LMi55Fb90Csn6FrF9C+DlSUnqdEusepLCSglqsW0C+bgReTO6D7aJLUE0lofcULYIap/juDU6RlKEuqPLklEggzZ/ufnYKe/ZM4ae7p6C0aD0IQppgp219URFtj0EWXyKhsYK6VLgS4KzIYUa2Q1DpfJMUeKCdQB3PoU7C5B5dEWZ39wu8AGb+p+U9ElYfvrsd2NmR2nYO3fafEb/+67+Or3/9661tv/M7v4N7770X99xzD97znvfg4osvDu+97W1vwz333IN77rln+QR1CZBaOnvdLC4aXrSq7L17w2DjAMfr9+Hqe6/G6a88HeohR1AvfmAW/3T75jCjOr86ROKEnxFJSU1ISEg4sJAA+oyxGsAUXHH9LwP4kH//zwD8RwA7AJzhfwaA6wD8EWOM+VmuyWDe9urnULUWgZjEfaJh987PpG4SqH+T7LjWcBjFQxgRzQVO6mXl3LhQm8iWGcN60kzhN5PUUgBRJYyOUnd9QJMnsEQ6rXZpuUYLZP2yTUb9frrKwKNjtNYcHUtOl2BesQvEE9lE0kpVM4Z6UQ2HKTNfLyOgfKov0FZPQ6pvBKpr6cL4JFxSNin5tqokqopmO4ncu78DUiOB5h/0ft/1wMbW3XgeVXdIdJeYdudJ3Xn0WEVQNyip318Mn6nqDGokoJVTZCk8KU5tngQhNFRFKcEcKvpvyt4I9JLQFfDTR/b/cwmrE0dtBu6YBd46B6wfOIJKr39GnHrqqXj00Udb2w477LDw8549ew54BcfesNoTfSdh+MgQ/yxuRn7/efgivohNP3c/Hrz/YYjrbsA5f+Cvb8MQyOcBpO6ZtY5EUhMSEhIOEKy132eMXQ7gXwCMANwC4H8A2GWtJYb2OICX+p9fCuAx/1nFGPsJgKMA7LWyIg6SISI4PvOpWq+73aKqkmOkU2sBrgwAZxWNSagjoFmLZOT9EnmvCiTMKBHsvUSeSUXtTagQidOAmSdd0EQkLYRXXcc6XasMRnH0jtwDJjWsEqFrlRERqzKITl9qfLXZ4a4DtXXcUQ5b+pneCUqzKWUg71Y5Mq9Kl+ZL5NrN69pAkLvKqjEMxjRfAnRBllgiZGXpZk+14VCaQxuDwltoBXdKKc2aGsORwVlshe8fpaqXSaDwJMBZhakiJg5TUtoFJ7lZ43GSGqcHS2Gg/M80M0uWY604NCmuE2zjGdoKamxLp2RprZdBUoE0X/pCwvqBI6R3zALHbQEe2tEQ1oOET33qU/jCF76Aww8/HMNoYPLOO+/Ea1/7WhxzzDG4/PLLcdJJJx3Q8672RN8uyLJ8/blzwJsGeNdVwD/gakAV+NUt1wIbgOEjCKrx8JEh5p+Yb9mFE9YWEklNSEhIOEBgjB0Jp45uBLALwH8FcPqEXUkpnfS/5zEVlTH2cQAfB4BjpmbC9lhFpf/gZ7lqzfItlYxKxAFAqP6I5wAZF2NESmY1GG+6SvNem+RxqSHhCHDRL0Oo0dQRe8bqaYDJSue+QOQ1RL4P+5D11xNXqwSY1GDSgBcqdKsS6OdJBNUaZ++lY5gyg67oIQOxnpQgXC3m4V4ZJYLiHCveRvsu1FoGO6xWHGWdQWkeiF2RKXBhPTl1ibicG0cQl7gX9H4XsTKZexLatQITAe4SVAAoinpi7Uy47ko6xbcGjGaN+ssb9TdeSxZ19MbElL6AMcslqQcp3TdhhWL9wBHU+y4BTt52UAkqAFx66aW49NJLcdlll+GP/uiP8JnPfAavf/3r8b3vfQ8zMzO46aabcOaZZ+Khhx7atwNu3+4KQuOhy+EQmJ/H9rcAm4/ZDACtGdSdu3euessvBSgNNg4wxBDy+JtRcrB10gAAIABJREFU3XMexGv/K2576s8xvOaLkFzihg/eAKB9/QlrE2kmNSEhIeHA4VcAPGKt/bG1tgbwlwDeDOAIxhh9KXgsgCf8z48DeBkA+PcPB/B096DW2qustadYa085sphCXWaoywzVYh6IBiXqSpo5lX7W0xNQzp0aWpeZ66CMSEJI1fUpwPmUe473obnOrKhDAm8MmiGVRZPUO3XEHkwdsQd5vxxL2CVSRw+rWSB43RRXuwQ5sZoHFdUo7p7LzF37dAneqyGmSojpyj38z/KwkQ9I8jZfw4ON14UnNem+8fvWK6JWCehStq5J5Coot7qS4WGUgMxrVIs5qsU8JPbGIPI1GhUY+YCiGFI4ktnv1chzFRJ988ypplI0Vl5CHGxUFHVQVml7nAJMXakzMwshMIn6SicpqDQ7y7kJvadu1rSZi9bKKb9VJWD875We3eyqDo+9gUir0eNugX1GSvd9YWHn0CmoJ29zz90Z1YOED33oQ/iLv/gLAM4GPDPjvlB897vfjbqu8eSTezXINNi82VXMkCpLFTSbN2PzMZsxe90srr3v2kDQZq+bxTknn7OqEn0nYetbtjqC+sgQZ10zC3nDHLa9+guYvuEmKG2wUC9AGYXho8NVT8gT9g1JSU1ISEg4cPgXAL/EGJuCs/ueBuAuAEMA74dL+P0wgC/7/W/0r+/07//1XudRI1SLOVSVQeZ1UE+zaBay1S3q02ppFpXUVbIEU8eoq3zR4MI0CbuG0nfjY9sQXmQ0h9Hchx258+VTJYQ/dmN7dQRDiEbvi0mr1jKaRzXQlYSIqm7Izsuladl/WwTVXxsvavC8IZuU1mu4dNtmSkC7OhkdzZIGgtqvAUpNLh1BNYvj9tP4OiiQSVcZ1EgCkE04lD9/XWateeB4LnNx1EO52CavzfFt6EKVQresuTHJk8K0Ao3cuU2YJY0JKqm1of6l576YsL5uhtY+KcTI6HaPrRQGlZ+hJWjDg0UZUC0lV0Zz092gr8nVRjrM6+4vrHWBVwkvEMQzqOsH7hG/PsB46KGHcNxxxwEAbrzxRrzqVa8CAPzwhz/E+vXrwRjD3//938MYg6OOOmrfDkrpQLOzwJYtrnfFx9kOgBCYtH5mPXbctaNF1lYzaSMB+drH5mHn5nCDn0G9+xnga6Mcr1//Zjyw+29xye2X4LzXnNe61mT9XZtIJDUhISHhAMFa+3eMsevgamYUgLsBXAXgawCuZYz9J7/tT/1H/hTA1Yyxh+EU1HP25TzVYo6F3VPuP/V+jlTmRIgoSKjp5DT+P+lETmmfSeQ0Rj5VNsSW71uPBymk2VS7k5UpM0buiHSG17qxy4pcIZtqQpkQ5mL9vn4WtXssqpJh3LS6TlvQVNvjzmXrpcmPrV3FTHitOXQ5/k9nqALSrEVEw+/Ad6JWi4WvWCn9zG5bISQSCiDMcBrNgGWIiPG8KIHUU604iqIOduEYsXJKSmZ3HpSH7fG8bbOPSyDmUIpDCh5IqtEMSgsQHScrMucGWgtIqInKudZiLPBpn5EqaF44eGq+TUhpRvWp+Z+ZpJ577rm47bbb8OSTT+LYY4/FZz7zGdx000148MEHwTnHy1/+cvzxH/8xAOC6667Djh07IKVEv9/Htddeu3+hSoOBI6iXXOIKQiPr71oMTAIaAfnss7fihj9w2878rSHY7CwuP+F6PPiPwD+Js6CNxhfv/SJe93OvwwVvumCsfidh7SCR1ISEhIQDCGvtpwF8urP5nwG8YcK+iwA+sF/HN207rNUcRnRmR0nFpECjiDQRIQUADt1SRSldN5796wYwUQgQpdYaJVrptSJ3vaPZlEveJaUTyGCAqB/VK6sRIWLKJ/MKHQhqrKwJfzw2wSIaE1RxmA9pol7TSEnToxwCjqCaMgtdp2T9NaUEj9/3Kb7N9buQJCKl2tfIAC78qRoVbu60o/qp2vWCMmFQ+i5VAKF/NMtrFJqPqamO1HFwLSBUm0yajnpZ1dIlAkcpvhoinGMU2YyJoNKzENoR8NCL6+dzSVFFQ1RjEmsER43Mq7SeXCserL0A/EytV+SFCYnSAJBnKpzfaB6s0K0QJW9NHo0mK817R7LuvqBw4gQljRTVnxHXXHPN2LaPfvSjE/YEPvGJT+ATn/jE8k82HDoFdds29zwYBKK61gKTCLGAvH69u+xzrpjHOW9z5POyh2dx/fuvBwBceeeV+OQtn8Q9P7wHNz98c7L+rlEkkpqQkJCwimAtaxEJJpwCZb1qxqVukUYuDDi3kdVXQ+YqkEs3S1qHdF0hFFDJlqraDawhotrtA40Jqph2SqpVHEwQIY2CnMquklqDC97qMyUyyiK1lWy3tM1q3lh8PUFlM35edlECI7d/rJpq5OHnbjiUNTxYgLs2XwLVzrh74ytXvEVWVTLc67jXsxvGpLVL8Y1RFLUjbxUC4XOJvhxGG2jBAeVswqR+kr2XCGtVZ5AdBXUSuSMFNd6vq5Yy0QRwaTREVUR/G7VfA5HOOOyJQGqq4I0dWSvRsivH1nGtReuLFwqUWlYFjUUKTkpYXaAZVG/xxWAQXg83oDWPOdgwWFPzmV0B+eJ/7b502P7N7WO25l/8k1/E1fde3VKTk+13bSGR1ISEhIRVBiEMhDDIe2WYa8z75Vgfp/Qdo1wYyKIO6qfMFZCrli01Bs2KTkqr5cLAEKGIiC4RVMCpjTF9tZqHmdK9gQmDrFBhpjRWgInk8V4Nq7h/iKYnNVdNv2nNgSyu6fEElepjJpDTGPS+WcyC9bRRTrPQ00qge6G8qkpfGmjFUdcuqCrrKNKkDj5XcFCea0hRIc9VqzLIGA4BHZHDcfU2RvxeXBkzaeY0BnXvup/Hq2C0dudVWqCqJJR2QUm6sxaqzeHCtsKWwvrCsUXruJwb1L43tei1LeT7BJEDh2/Y/88lJDxfmJ9vCCqA7fk8Nl91IQbz85jPEWyt27+5HVvfsjUEJq0FkrqUgNwlncNHhnj4mYcxlU3hs3/3WQw2uGtPtt+1hURSExISElYRGGtCjKjDFGgHG6lKQuYqhBcJoSDQzBHSdlKuKIApqKkRoTIQre5Sqxm40MHiSkpsNlW2gpBIgaQZzpjAEqzmYdaUrL1iqgyBR7xW0KPcz5d6+ykMtGrCjqhShmUGrFBg0sCWEkwb2FHm7L61T+XtzJ5axVsqLb0fE9R4vd310/1z9l7WqpchiOheAUA5KkLwkJER2fOhRmSbJUIqhcbhh+9Gv1+Opf9qJUJqL+DnO/1zvJ3ei2dUaZ9wrIiMutc8zO6qzn8VSAU2hoc1VdEXCmUtW0qqlAa5/5vKfeK0iCzJPB+fbw332IcvSTG5Buc5oSvYXd/b/88lJDxf2NomZJuP2YzZv53F3AfmsNWn38ZkbLBxbdh99yIgt9t4/PVTFc1ZXzoLv/pffhW5yHH9B69fE/ciwSGR1ISEhIRVBMZsCDvKp8qxQKMQkuRnTGNLLc2cirx2NlmviKoyc2qoUOEzBLJ2BgIqnMU161ehH1T2K4i8BveEk5RTwFljmWhmSzkc0Wudo1DgvToEHlHCriOZ7jixXZbnCnqhAO/VgcCKfgVWKKeg7s5hR5mrjYnU09Z95AYWPBDVuDPVLGbQpRxTf7uBPqrMAjl1937pmpSYqNJ9z7IadZ0FgtpVQoWveJmeHqHXX3ThQh3FtBsmFBPArkoroAOxpbCiGLT2RjkVIbk53lYuFihLIqm9oKACzQwtQUqDIlPhWsbui+YQpt31G6OuMhQ+4KvXXxx7f5+Q7L6rHtba/QseWgXYxyB3DDYOQqLvllO2jCX6rnZQqm8sIPtaWMzNueeYpMZ9qgBw/hvPxyW3X4I3v+zNa+aeJDgkkpqQkJCwisC4RX/dyM8T2pbF1ygxto2qWYBmfjJYWFuWXR2IZghQ8moekVpCILl9dx7ZIR8xKZVT7T5VoJlLZcIEosmLth2WEnVFvwodpuHzhXIzuN0U32iNRFCXSu61hod04DDfahxp1aWEJqXYr9NqF5hEzzR7SoTRqMkEtbGvTk7HzeDuXTxb2trP212F0KGDlLbHymhXOZ0Ezg14boI6KZawB5P1tqaZ2hrIMheuRQFGNP/q+lBdJQ4AKIhWFY4UBlP9KhDNvaGr5tJ1/UywSMFJqxy9Xg9PPfUUjjrqqDVDVK21eOqpp9DrjfciT8JaTfQFmlTfmKB2FdUYsfV3+MgQV9x5Bc57zXm4+eGbMXxkGLpW02zq6kciqQkJCQmrCIwb5L1qrDLGaB7IKSXy6siC6dTPMmw3WkD51FqyBlNoEZcGTBkAUaqtf44rYYjMhn08seTSkc8Y3RqXJbtMSwlrGOi/oqxfg2ca1hNVsu6KfhU+x/o1IAzs7gKI9g33zFuFTSnDOqwSYR3tdYoWITdaAJ6wW819mm/zoPnTLmgelRJz47ChLhnLUKMssxbJjDtUR6MCyif/Ki2QZy5wyFSeyEvXO6rhVVY1mdyRktmooEWogOmClF0ilrQ2siTHZFp2iKWbP3W/g36vRp6rMasuKbpj922JSpvue/uDfRSsElYojj32WDz++OP48Y9//Hwv5YCi1+vh2GOP3ad912qiL7DXWti9gmy/lwwuwWV3XIYL33ohZq+bxYVvvRCX3XFZmk1dA0gkNSEhIWEVgXGLfGo8QIYspBRiBBAZdXOTMlct62q1UMAYhrxfIeuXEIVqpepSIi9ZXrlwCmJsByYy27XB8qJuzZECgOW+6oVUVK+e8kIBmQ7beaYBT0TZka5KhpEaWgtHQmvRJqg9d73BIlwvTWbiOpmx97zNV3X2IfUUgFeYBVSVQXklkWy+QuhA6ozhgcTmed2qchEdO7XWrrO0m/Ybkz+y2BZFlIjr1dSM15DCwETENQYl6dLaiqIORLVcLFBPUDhjxRYA6s52FRFGwU0rKIkLiwLKPWc1ZHRfwpo6xa9C6EBCiRxz3rYZLxuJpK5qZFmGjRs3Pt/LOLggz2tr+NJ5Xocf2LymE32BvdbCLonY9rvp5zZh9rpZnP7K03HR8CJ85dyvrJl780LGMvLcExISEhKeLzBYl6Q74QE4RdVqBqtZK9hHVTLMihotIIsavZlFFDMjZFMlsqkScrqMal90CDMKoUaebHG5tG1TFAq8k2QLODUzJqYsM22CKtyDFQpspmxqZADAhyLBE1Nn83XMwyru0nxrb9Ud5c626ztPyfJr/fvG23lNrKgqHgiqrrJwn+ihKxke1UKBajFH7YmsI6OkmLaDimSuUPTLUOUihEbeK5H3Sh9U5e+rnw8lRTQmp0LqsL3fL8Ncajh3SNflre17s/6SEjoa9cLn48do5AgxEVk6ZlVnUFq0CCqAibOmRFDjJF96dMl4uFahW+ppS232Xar7D+ZmUpfzSEg4VCDP63DoXg+H2H7ZezA8TrbIGNlYKdF3raCb6ku3YW/Y+patrUqaLadswdX3Xo2zTzi7RVCHjwyx/ZvbD9bSEw4ikpKakJCQsJrAMRZSRCqd1QyqylBN6MU0SgT7r5s/dcppQ05NExzk92PChJlM4ecyuwFIraVJl84boxV4NFM7slmLUAsDwBFUSu+l2dTMOOJJ1t3MgEEB0rSsw6iFU04zDbPbXTdVyIT9/CG6Vl6aNQ0hTxUR1fY/jcYHCKkyCwSVgpY4N+E4RKLIqitkGZRWR0INcm+5rkYFuHJKqAYaFVYCUIAus0BWOTfI+yrMedZVYw2WQmM06rX2reoM+V7UUaU5dj87hbLOgtJJnw2VNsb1mrrr0IAC4FOHhTQhfZhmTfNchYRfKXQISiIFNU4llkK37L9dlTVWUHkg+AbPVWE0EdZ1CyckrGhM8LxuvuoSzD58GeZe1xDUlqK6RpTCfU31nYjvbgeO2ozhAiD+8Qr83knvwK0PXY3bb34ap57+Vdx9z5W4/c5tOPVdXz0k15JwYHHQSCpj7HMA3gPgR9bak/22FwH4EoANAB4FMGutfYa5SfjPAng3gAUAv26t/Qf/mQ8D+A/+sP/JWvtnB2vNCQkJCSsfNgQVdWdCRa5CimzcYUqvqWpG9ivIwil5wte2xMFFHE1HaXdmk8VzhF2SWtQti28XjFtHQqUJn2XSEVQmDNBXsL4GB5kFhAVTDYkMRJbuRDznGqmmppLB1ru3dN4YupTQpUTtK2KAxkJNanSXoALN/K8RPKinMWRuwsxq3i9DVZBRHFoVLYImhXFcECIQXR6RPeEVUrNESFJ8rKrOkI/t4VAuFhgt5uCi8cHG1TRkBxZyPAG4IeCmRTZzIBDXPFfIs3F1lVKAD5tZaHW1ApFdfQJBlblqWan3G4mkJqwGdDyvg7MuwNwjm9Zkqm/sbqYUX9q+devkVN8AT0yxfgActRnVfz8Lf/VkjS3/6pfx0mf+Br/1sh7e/Z2v4R173ol/M/oG3vumy7FpDdyzFyIOppL6eQB/BOAL0bbfBfBX1trfZ4z9rn/97wCcDuA4/3gjgB0A3uhJ7acBnAI3VfI/GGM3WmufOYjrTkhISFix6IZbcqHDDCUpgGTvlf5nYxhkrpD5qpjiMD/rGRO4mgeCGWpfqIOUyEHekFcAQc0VhZt3ZdK0jhMn74Y50VjJlAbo+VlZaRxBzTxxEhaomSO09JnFyf9kmQkENYQ4xfspHip4AIQeVKs56oUC9aiAaoVNUXKvu5+cG2RFHay+hP66UdhWeKU0Pg4ipXJs7d4u3A0Mii2/WglANl2q4XOUHKw4tOKBzBJZjHKvWhZgzg36PWennpS6S9ZisucK+EAuTxQnBR4JqYO1l/Om0zS29gpuIHi771RIZ4Omey1E88VKnteQfpY2Vm/3C7IAjtiw/59LSDjU6HpeBwMMBmsz1TdO9N26ta2mAp1U35iUAu7n288CfuGDwBv/BHesexsuUV8DmzoSeMaiJwucv+E4/MroVvzNEe/A6a+7oDnxziHw1DxwYkr9XQ04aCTVWns7Y2xDZ/MZAN7uf/4zALfBkdQzAHzButKobzHGjmCM/bzf91Zr7dMAwBi7FcC7AFxzsNadkJCQsBphtSM7RnMYw0I9Cil33M+UZlMlxLQjUqREmsXMpfF67kDbea7CLGkMU0kwqV06rp9XJYXVGg4G066GoTUa1lZQe6pt7/XqKQDYzIJpC1A15qKEHWWtkCU6Zpg/VRymdJZdwJFwo3g4p66yEA4FAGohh9EiKKiqkqHzNFwr9c5KDc5teE2kdPrwPZBFjWqUwygRQq241FBVtqT6p7UI6b+LowJ1lbW6UoteibrKUFcZjNTgETmtKzcvGo4VhxlpEV5XdRYUTa0EVLSWoM7KRqGsq6z1mogtzbAS+QyfFRp13cyuBlU1U4HI1lUz10qfiwOc4vsTp1U7BbVGlitIfw2MLyM8SZWwz3xv/z+XkHAosYTndXjVhdjx8NpL9d2vRN+jNgN3zAJvnWuIKizwL18C+uvxywvfAjb8r8CjVwMnb8OjzzyKM79/Ne7INmLzrm/g7nuuxKbXXYC777kSx//jRZgafOVQXmrCz4BDPZO63lr7AwCw1v6AMXa03/5SAI9F+z3uty21PSEhIeGFCTY5qjS2qRoloLUI6lxvZoRiZgRZ1JDTZbD2UuLuJMQENSabzPBgB47JKVsiTClUyxgO1G7mlEkDNl0Bfb8OUlAjggogvEbNYUcZTCnBDIMbkARQC6ealhJm0YcXlTJYoFsqKm2Tbs3UkUoEldKOAYCHNbPGNk39s0o0xJ8bd0/9a1VmLbsvfZZ+jrdbwwNBXRz1WvOmWe7SepHXKBcLN6MapeGqiIjG6CqNWvFgQxayCSUSUkNAj1lu4a+DXisg9KrS/kVRo+iVLUuuO1dcn9McO/PXQNbg7jknzZ4SOPdhW3sJ6tonpBCkhJUO8rwSSxsMMLzqQszeexHm/vVX1mSq714TfWP1dP3AEdTbzwJedAqw69vAqTc4VfS+S4AN5wE/uBk4eRvq716JI9UCvvWid+BVT30D973413Dydz6Jbz3xdbzyqW/gwVdfjk3rV/+9e6FgpaT7TvoXxO5l+/gBGPs4Y+wuxthda61LKyEhISGGC/JpHrrKggpICmpW1EGNKmYWXUDSVOXIJ2+rozxXEP0KYqaEmCkDQQXgbbQiPCahS1BtPf5PC5FdUlDtTA3bM7A9A9Az7VszsJoB2imvtpQhtdeUmZs/LaUjp56g0kyp8Z2m3ftF0KUMaitZfCmx1ygR7NGTVNVwv7h1imA088uFac0Kc+Hel7kaI66qkqgrCR1Zn4m0ZXkdbL3GcJRlhtGoQFVnqGr/s5/t7CbtasOhvQ1YeIssqZ+kYMYPKdodqUXhyLHSPJBmd73N/lnWTtkVQqPolej1F1s2XrIKx5+fdM6lIONjkRK+nBoay5b/SEg4VNi6NbC07d/c7lJ8j1aBoK7FVN+9JvqSeroz2mgqYOdfAcdtca8f2uEI6qNfBE68EFg/gLYaU3IKv/SLF+KxV1+Ok5/8Cv4x24Bf2nUr1Ppfwaau9fe7KfV3JeNQK6k7GWM/71XUnwfwI7/9cQAvi/Y7FsATfvvbO9tvm3Rga+1VAK4CgFNOOSW1oh1EfOdb30K1a9c+7fvIAw/gF1/ykoO8ooSEFyaIlNWj3CuB7W5Lnhvk/QrFugVHUHsu2Ij5rlEAvhKmDgqnVRwCzQwp1bl0iWfoO/Uzq6TIWsXB8nESQr2m6CmwmQpmXUN0bGZhBcAy2xBUAOxZCbsnh/5pv3V+XbsU33j+lKp1ugSVQNuN4mGGNyao7X1JdaVZ0ihgyG+TaKzAMXhE4GSuAomVuQpfIJSjAuWoCKRL+CCkODiJbL1EOlEDRrNW2NEkxKRVCldf81ykkJTPLHddrcqnQ0vRnj+lfajbFXA9r6R+ap9wXNfZ2PG7x9NKjIcyTUjwtT79eLmwAGz6H0HCKsLmY9q9qGsp1ZcCk4D2DOrOnZ1EX1JP75h1pPTBzwI8B074pPv5wT8ETr3ezZduuhz47mXAy85G75dvcgd8ah6bXrcVNz/2dbztJ7fiDrkRb33yG8ADVwInJOvvasGhJqk3AvgwgN/3z1+Otn+CMXYtXHDSTzyR/W8Afo8xdqTf750ALjzEa07ooNq1a5+J50N33XWQV5OQ8MIC0aIWQR3lUJWE1gJFv2wpd1m/ct2lvbqlnrJMQxBxjLpKGQDUtmVZoUoXAGPzqfFrq7gjrRMSfhm3rv9UONXU9NrMwWYMnlI4krroFFSzJ28RVKqWsUqEXlPCUgQVQKeqh7cIal1mLrAnb2Yxl5oljecm0VFa4/dCWm20zRiOalSgWnQkkAKDqHqGLLNEWolwGs1Q1u6fa6HbSiSRVsENtOEwmiHPVWv+sxt0FF9bsAD7HlcjePv4S9wHIqbUAau1gACghQmzzUCTWNwlvNR7SvdHCB2OFUMrDpkbd/xl9aQi2X0TVibiiFvCcIjB/DzmPjC3JlN9KTDp7LMbgkrk9JxzOom+6weOoN53CSCmgLd/1W1b3Al871q3DwUgvWiTI6xk5V0/wN33XInNu76B/7P/Dnzq/m/gr9/wa3j73Z/EDx77Ol62M1l/VwMOZgXNNXAq6IsZY4/DpfT+PoA5xthHAfwLgA/43W+Cq595GK6C5iMAYK19mjF2CQDyNlxMIUoJCQkJL0RYy0JKbT3KocrMBfRIE6ylQQH0YUliuhwjl0AzHwrhE3S1J4D+P/VxIFE4f2dKJFZQAUD0q5ZSG841U4LNVEDmSKojpeMgmy9bFDC7c2fvjc5BXaekngKOnNLcIpHRmLDS/QKl4VYyBEwBaJEfY9yxibCGWVR6P5r7bb2OCCkTFtDe2huOy1CXGUa7+9CaQ+bKzdAKM2Zjjec7naWXY7GU0IahyDUA1SKSea4ghW7ZfOlaKF1XiiZVN7blAq6SJgaRZDoG9adOIqxWO6XTag7t63q619HvNzOspLISSe0iDm1yazGtyp/lIPWkJqxIxBG3g0ErPGmwcW2m+saBSevXR4FJ690M6iAm7A9c6R7rTwOejizOb/gT4OXnjJFSRIRz+MgQt9+5De990+W48HUXoHjplfjlWz6J24/fgLc+eSuePvL12PTzm9qLS8m/Kw4HM9333CXeOm3CvhbAv13iOJ8D8LkDuLSEhISEVQtrXUptQ1BlmI+M7aVcGHChIYu6CUryiqStBXjhbLcBmYHdnbt5z1pMJKhhDZ58uACi5n0xXYHPlM42PMp88i4Dn67AZirYngYy6+y9ksFERJXXPoZgEUDNwtxpc07esvYC7VnTropK9TgAgtpqNfNzvG0yRfsSMQI8OUM7HCj+DEGVWZhFBTxBDcfVYca1Wiiw8NNpb4n1iqHQjtgJv05v86WkXOnX42ZNGZRmQCXaBFXoUA2TcwMjeKv+JcyG+nNyYZBPIKlaC3DddKVSUBPV3+wNRFAnIctrZHndOicXJlT1xMcQQgeCjXbd7PJhkeZLE1Ym9hJxO3xkiB13rb1UX2CJwKSdnQTfB64E7v6ks/KecIEjkPH7HVLaxfwT8zj1XV8N/agXvOkClN//Oo5fuBW3HfE2vH30bRfEdOr17jjx8RNWDFZKcFJCQkJCwr7AMuhKQpVZUAJlrpD3q0BSZV5D5AqyX0FOVSEoiRcqKFKs72s9OnOARFBtzV1i7oTwIF3K0KFKYNK42Vbalmmn1BYKbLohqGOXI9wjJqxMM2DRzZsSTJm5oKOFwpHzTldpjDgNVlcZrGaBoCq6d1FnaPcz4ZxL2Hm72/e2FhfClGG0px8IqoxSbuM0W6Ctoobjaw6lGbT/fcehRlIsbeWNA4uILDo7uEskpt/7JIVUSN2aH6UamknhRVrzVpo0fYY6UMnKy7yam3sVlR6A+zuM1yGkU4Tp73WpFOp9AX1Zsr+PhISDjpixbdkSCCrNoF48uBhz73fW3+Ejw+c+3grE9u3tUKThEPjDPwRtSreIAAAgAElEQVROOy0KTIpnUO+9CLh3W0NQEb3/1L6FRm19y9YWqb/7nivxb0bfwFePPg8fePAB3H3cpwFYR1TvvWhCxU3CSsChnklNSEhISPgZYC1z1lJfh8JzhXyqbAf29Ctw6a2+U1HljP+PN4uCgEiVRM1DYq7bt1EsW+fXZL1tCAnPVXOOuAdVGDCf5BtXzLCagSlAKHptwTTAFzjYHgH7TA/q6ekwC6v3OHJKyiitYSn1jnpSjRawuiEbRvOgbBpPumKrMNlaw/6GQ1XtfybjmhpSSmM7MJ2PzqWqLHSqFv1yjEQyYcA7lt+yzKC0QFVJjBYzKOWUVMEtilyjyBq7LyX8tlapGpLJucHMzIJTZnPl0nmlgVZtwllXWZgtpX5Ud/xuUjJHVWVj29zvo/ms0rwhuVpA6A4Z75DdlorqwXwXq4b7e1T10l8GLAlZgB/1C/v/uYSEQwEfcbv9otOw+cufxWAwwHzuUnwBl/S79S1bQ6rvalRTY1czAJx1lnMEfepT7nXjeI5mUE/e1hBUwnOop0uBrL8zP/ereP3xH8Hc8R/BO6+bxS3v+DRe/vif40V0vkRQVxwSSU1ISEhYTbANKSCLb9b3gUR+uyxqF5ZEVl/fc0p0LaidFEjkZ1G785/hlPGcYSlbyisRVD7j7Zu1cOfrORXV9rSrmMkigloz8EXTvF4UYIscGLk0X/X0dKv7tF4oAvFkwXorgjrKhYbI2/7QcYIqwhyqUQLlKAov8kTVEX+EfxmJtFL6bzybySNrdfe87pn7GhtHtDO/vlgZpF7TPK9hNEeNzJNLRyIXRjkWS4mydkrqVOEIap631dOFUYEic8q4kMYtXzmr7czMAvozI2f15Y06KX3/qaJEXmRhXjSuwAm/Z04JvpFaGtutfV0OQXaU0bA9H/fx0toILKjMOjyr6H7tF1QF8+Rjz71fQsKhRjSDunkDMFuchbnfOhNb/+AGDIGgpgJY1am+sav5ta91BPWGG5o51Lm5gQtMOnHoUnvXn+bqZZZJSrsg6y/Q3NO598/hr++5Eh/efTeePvL1eNGDn22fL82nrggkkpqQkJCwimDBWuRI5MoRUvoPfaEgphv1lFM4EjA+X6obQmqjzlE3azpu6yQlk0szZvcFHPkljZYJA2RmzOJrOzUzfJd0s7CLEnaUQf20B71QhHAkXWUhyThWi4mghrVF7y9JUH0PqqpkUEit5JBZHdl+NUzVvk/VqAiqHuCqfbgcD/1x53KfpXOoOoNWvGVd3ReMFnPsGeXB4jvd05DR/e7afAFPUEVTJ1MUdZOiGxFUUkyBRo1uEoX9vG8UukTpwwqAiPpP2wFPbnueqaDg0gws3TfZmYUl0NomvufnhRk3EMv8H0sKTkpYkZifDzOoAwBz516PWZyFLX97KXbc9e01k+gL7H0OdfDWOQxOhLPewgIne4n1AFlwt76lIZpknd7+6tPx4d1fw2OvvtwFKN1+FnD7mcCpN7TPnfC8IpHUhISEhFUE5mkgBSOJvIbIa0cchYGYKiEPW2z273eIQS1c8m6mW9tMmQXlMnyW0mqJtCoeFFrqRw11M7VwGTWlBOMWdlGCAWBQsMKC6Yis1gzML9HuzmGf6buQJsOhvWranRHlQjsiCiKdvq4lUuZoW6ywNu95FdXbRoNKqADGBfSoqTiJFUJSPGVuggqYT7VDf1rniGy+Wot9Uv+05qjrDFUtUVcZqkqGyhkAkML6VF86D4OCcD2onuDJQESbTtIsq8PMqzHchxFxKP9PP62xrjOUZdZSTuMKHFJmIZ0VWQoTyKyMjs+5wdTMQlNJE5TQ9hwu4BRVureTCGqbSLtj8WIyyd0rLFIFTcLKxNa2SjfYOMCWN53vEn3fvHYSfYHgasa2be55MPD2XppDPeK1AKwjiURKaQb1ANpwKTX5ge9cAvyr8/CR13lL8anXO6J636XArm+n+dQVgkRSExISElYTWExQlbepGojpEqLv0nX54Y4BxvOmAICoXoZFFTHGq6hh3tQ/M9FWTAUwUUG1xh83zLMyMM0boioNEM8kRusxu3Oon/ZC/6layFt2YloH0CT9kUqaz4xaKb5W85a6Gq7PW29pPrRLHMniTJZSImvxjGpW1KHeJ077nZT8S92rtkX6OIQc75m12imWZZlhNOpBKx7IIQAIbtErVOhB1abdYyqkQZ45xTTeTkFJk+BmbTNUVYa6yppQpAnqOYBm5tUTVeWPoZUA/Dlo9nVqZsFdrxahDkgIHezOACAzdy+N4WP3qXt/iKBSvdJyYMfzuhISVhzWWqIv1cAC7ZnUnTv3MocaE8MDZPeNQfd4yynbsPWuHdhw/NDd4/UD4Pjz3To2nNc+b7L+Pm9IJDUhISFhFYEx68ip0BCFQjZVtghqXCsTz6kCCCoq9U4yr6ZS1QwTZqKKOXEdXbIVkV6WadhRBtavYRclkBkw1fSYurW4Xlb9037L3hsqXZZYwyQS2iaqbGyfxurLx1JiG4XRdXhSnYqIUo/JLhsTVAATZ1IrH/DUDVxq3SvTWGpJRSUFVWkB40k4kVMin/TMhQ0qKoCgnhKE1BMJKhFuVWVYHBUh6Ch+vztHSkpnTFSpKkdpEay90gdAkXU3gwpfBixl5Y1Bf09ZUYd7TeFW4d4vYbHeGyyS3TdhhYBYW9wFOhwC8/MYfmBzmJccbBxgsGHQer0aQYFJZ5/dEFQip+ecg2YO9aEdjqAewDnUSaDU5LNfdbb7AiC6x0f85G4c/49XYmrDecCjXwSOfN149U3CIUciqQkJCQmrCcwGe+8YQS1Uu05G80a17ECPcrDSq4CeUDGpwT1ZBJxySV2oNKNqyiwQTiY1mHHVNkR8AV9jA4BnOqzHLvreU09mrWGuVubp6VYaL5cmVLoYLVrpuwYA4uCeKgNQT+5NJeut5o44RvUpQhpANaSNnuO+T+btsgCCgkrHjckpKaghNdhwyFxB+7obImhaUfAPD2tRlcTCnimMRgV27+m3CCoR0DgkieZQ6T1tOARM6DJVaAKLGusyB/xsKf0uuum8wv/eeUR6SVWl9dO5A9GN7rWMqmVkrsK587778mDMPq0FuGkHJQmhwSXZgl1yMnLlw6cmK7z7BMuS3TdhZSCOuR0MWsFJ80/MtwjpYONgVSf6Au3ApPXrnc33W5/bjlecuBlYP3AE9Y5Z4MQLAasa6+9BstrSPQbaAUr/cM+V+PDur+HBV1+OTa+7wBHUuz8JPHMP8IObk/X3eUQiqQkJCQmrCIzbVnqv6FcQR4xcmq4wToFcIiCJQB2ogCOareOL9uetEmP7kOrJNQf8eCYR1RZqAfgAJ1JNqVaGiK8uZSClVJvDBR/rHqX5Uqs1tPa2Ys08UXXvd+tmABdgZLrrgiNfpprc+yk8QSV7qVNSLbgwLVsvvSYiZQxH3nNKtjFt1TYmq2S1XdjtCOpoVLRIH2GqXwVCSseIA5PizxBRhSeMMUI4UvRzV0UtinrifQIaghrbn0lhpTlY6kKNlVDOLVQlwc04WTXR31hMUMnWS79vusdGiYnK9b4gKakJKwIxa9uyxVXPXHE2Nm8Atm5srKTDR4aYf2J+rOtzNaIbmPSKN2xuiOhT846gfveyhggehDlUwqQApS2nbIH44V/jsTd5ggo4BfWZe4BHr3ZJw10k++8hw/LbsRMSEhISDjkYXIKvXDeCXLfoql/29p/3KMGXYCoJ7WdQTTm5e5KIqNV8LOmXPqtLX7FC3agRoXJk1JODmkPv6qN+ehrq2T7qn/RR/7SP6tl+S/2MA6CyqRKyqJ/Tetz0korwTESyWiiC/dZ0iTudU2gfMKT9/KQPoJJO3aN5yBimQ/opMXhfUNcNQd2zp4/RqEBZZzCawWgGLqy38z43IZNCO9uv70QF2tZf4a9H+B7WygclhQojPrkm5rngbL5uFrbfLzE17Wpu+tOjFkEF0LLoCqEhs9oRWZql9gTVrcWOEdHlEtM22DIfCQkHGDFr27IFm992Dmavm8XwkSGAxpK6+ZjNz/NCl4/t251IDDSBSeedB1x5JTD8bhSWpHa3CSrgng8B+aMApUtuvwT6Vb/dEFTAkdAf3OwsyE/Pu0ClncPmvTtmgaNW7+9nNSEpqQkJCQmrCcwie9EeiH4FZBqsUG7mk+ZPOzOfdtSQUFNK6FEOU2aBDAIIJDQmpuE1EBJiwxL8uZgwvo7GhBlXW0ZhQbsL2FqAZRr1M9NuDb5WhsCFbqXwAr5GZ6qEqSTqn/YnzqECTe1MTFTjhN3Y4tsFzUty3qT2qkrCiIZwB/IktbPykqIXES8uPCnzIUB0zDigqTsLWtdZUE913EUqbOvndoDSOFkjFTMmmq2+0ej3VvTL0A0bjhkn6Kr28yTQHCrgCHK/X6LXX0TRL5HlCkV/cuoxVdgYxccIP8+b6p/uzG/Lvm3YmLq+L2AyB3/xy/b7cwkJBwWdmNvBYNBS9XbctWNVz6ECjav5wguByy5rni++mNzNAwyWCks6RKAApdM2nobP/t1nm5CqnUNU//0s/OVRH8Q5r7nYre32Mx1RPf58Nzeb7L+HDImkJiQkJKwiMGEgDhuFgKRuEi6AFkG1hoVQI+pBpc+RjdeUWSshl0CVL0RIg+1XRQRVmFYIE+PGzaf6ahpTSphne2MEGGiUUwBh9pQXNcR0BdGvfKCTgFrIw3wqEwZMWJiKeytoew7VHcvPh/oAo7ijlNYHNAS16JfNXCrVosRhSdz64zXnagUocRvmQOsygxC6ZTN2VS8uxXdx1ENVS1S+P7Vr813SzuvnT5tzmlZo0Vh4kk/VjUmr1RyLnqg29TB+dhX+b8QHFdHau+shm2+W1+j1F9GfHqHol/sUttVN6DXRrCjn1s2z5mos/MoFMtl9VqtjWFVB/+jx/f7cvoAx9jkA7wHwI2vtyX7bfwbwawAqAP8E4CPW2l0HZQHPMxhj7wLwWTgn+f9rrf39zvu/AODPABzh9/lda+1Nh3yhKwXRDKrvYAFmZzGYmwuq3rZTV3/1DLma3/Me4H3vcwSVLnnTJmDnvUPg2EMTljQJpFbTfOpZXzoLZ37pTNzwwRvwC9+/Fr/9A4vf3HyO23n9wNXi3HdpSv59HpDsvgkJCQmrCQxOPfWwHespfEBRTFCtn48km+8kWN1WOGNMssoSoezOq3ahFwqnhvpjqDJrrYGRvbYb+mSYI7vS2XA52UKl/7kTXtR6eEKpak/I47lObzmNVb/uLOaYvdeTKReM5I7dtfwCvnbF97ASiKAujnqBoFLVTFdFjQkhKadKu77SPFet98NaRUNWi14Z5lFp9nSpOdP29dGcqWi9jmdRw7qkwfT0Avr9csmKm0kgwr/0Gti4jdr/PgnLraCBZct7PDc+D+BdnW23AjjZWvsaAP8fgAuXt+iVDcaYAPB/ATgdwIkAzmWMndjZ7T8AmLPWbgJwDoD/+9CucoVhfr5ha0Bgc8NvXduqniHr72pCbPEF3KW9733A1Vc7d3O45BOHOOdYP5P6mosb6+/OQ3fNcUjVYOMA13/wejAwXPo3l+KX7vxL/OZ7bxj/omDXtx1BffSLwANXum3J+nvQkZTUhISEhFUExm3b3osoxXcCcSILrq2jhF7Fo+Tebl1LO9zGKA5Z1JjUoRr2qyRY6dRKM4EEM2ECAaZn4XtfiOQyacJsq96Tu3Anr5yJQrlqHDqf4BC5Qj3K/Zqb66YQI1Ld4rRZACGgB2hIZVCRfUIx2WC5cSnARK6Couqtp1ST4jpY5VhwFFlp6yqDMTwQ1MqvjdJ8Y5sv4AiqkAYFapR1hiKrISNV1BiXECzg1pVlNbgwKPolhNAofb1M1+qsNQ+BSbGtt6pl6ErtghRbKABaYHp6ATMzC+DCqdBxvUzol/Xr4lKH3+HeQAq10Rya7k30ua7Fer9gD15PqrX2dsbYhs62W6KX3wLw/oNz9ucdbwDwsLX2nwGAMXYtgDMAfDfaxwI4zP98OIAnDukKVxq2jqttww3A7F1/ueqrZ7rBxVdeCXzxi8A1F23HtbdsxnAwcESVwpIoHOkghyVNQhygBLj51PPfeP5kJTuuoFk/SMm/hxiJpCYkJCSsJjD/P25pwLA0QbWGOUIbXnfsvNK0SCsh/jmudmHKgCOy5fbqYJ21isOUcqw7tYt6oYDVzNl1FUc21cyzAgDLo+N5ksJzBStNmI8FHNG02qmp3YoSIo1ai1a1CYESZAHXyVl5Qkf3h4KSuHFzpVlRt4gqncMA4KSqTph9DeqkJ4Zl6bpQyeJrdFupa/eeRim+NUKKbhecO9stEdRepA4v7J5yVmdhoDqJvloJjLztt1spE6u1rZlXCfSlxszMwuQO1rgqSAnIqdIfw2JvfxX0++LStIgp/Ux1NNz3sO4vLNjzme77GwC+9Hyd/CDjpQAei14/DuCNnX3+I4BbGGP/O4BpAL9yaJa2erDaq2fi6lcKLt60Cbj1VuCKK4Bz/pfNOPuEWcz++zng9wYYnLh5vHf0ENt9uxg+MsQVd16B815zHnbctSPMpw4fGYI98J/x9piIxsm/z9M87QsJye6bkJCQsBpB8501b+y9RDDr8fAbG82DGi1CQm/XLsylgSzqUHPTnXkNNl9uwDI/j7rEPGKsLLp5VAbZr5BNle3gpmi/OIWY5yrMtnbXMSlMiRRNLl0VSkiR9Sm2MiJXRGzjWVUWK5UUeqT4mMJISqaqMqhKoi4zWMNRVzLqQ3Ukn1TUuspQllmw+FKKb54rFFk9FoxE85+TLL5ETouiDiqx1c15tRZBDda++5VmYrUSqGoZroHOBbTtvUuRYgLNvNL52l8EjNtyaba3q4gSwXeqvWip4DRbHPad4BTYJyzf7vtixthd0ePj+3pKxtin4PTnP1/eolc8JjH/rmZ9LoDPW2uPBfBuAFczxsZ+iYyxj9M9/vGPf3wQlrqysP2b24Oll2pmho8Msf2b2wE4otpV+1YqSEEdDh1RPf10R1Df8Q7gggsArB8gH8xh7jdnUTx40UHtQV0OaD71ksEluPnhm3HhWy/E7HWzuPLOKzF73SzsCb8zPoNKyb8PXNFYf+P3v7v90F7EGkZSUhMSEhJWGxYlrDAuHElxoBZOOa2Fn0F1/38Mz97qa5TrH7WaAwVCxymB+xqWQDjKZjuRSt6rwQvVqKYZwGBg63ECYRbd/KmuMugqg8gVisNG7j3l501JjTW8RVDFdNUKZKLAJwpgGjuXYS70R5qoq7QJO4prURoSxFtKa9zlGT9zadxxvL2XSClBK47az6HS51QlUVcZ6tqR06p2BFVwA8FN87M/NxFEIqcAUNYZpNDQiofUYVI3+/2ysf8SCa0zGKNbiiStx83qcijdWH3p8/F/BCaR00k2YCYckdWKQ1JCLwVNRSm94f5HBJVs0nQvu2o37WsmBFYtB9YsW0l90lp7yv5+iDH2YbhApdOsPVhm4+cdjwOIY5OPxbid96PwM7vW2jsZYz0ALwbwo3gna+1VAK4CgFNOOWWt3q+Azcdsbll6u0E+qwmxgnr66c7ie955wM03N8QV6wfIT9yCN993CXDcylIfYyV7089twux1szj9lafjouFF+Mq5X9m79Tc7wll/Aaewxu8nHBAkkpqQkJCwmkB236heJgQkLfERa3gIRtJVFipb4BtJqAImBBj5WVVRKDDVKJlk8Q3qY6YBEicztIgqhTTpqglKyteNwIvaH68hHowbmMol+TKpnYLqCSrj1qu1GiilU4H9XKtbt3Qzon6bzJ0KTFBlBmNYiySZSvpAI+HU1ijNlhTIeI7VKO4qVCqfUOwTgEkFtJ4kxogJKtl8Y1IqYFpzpgQJtEKV4gAl6kQlghqvUWsBVIAVEwi8J7C0FiCaNQUA1Sai3aTgQGZFrKSO7yNzNUEpdX9rk7ZPSus1hoUUYCMMKj93DGB5ZNXikNp9feLtvwPwP1trFw7ZiQ895gEcxxjbCOD7cMFIH+rs8y8ATgPwecbYCQB6ANa+VBoj9sN6DB4F5tTZq7Z2pntJlNp79dWOoH7hd7fj7g9txjtnB25G9cQh8OAfAutPe17SfPeGWLGOu1Mnpiw/Nd9WgU/w3ar3XgTUu1I9zUFAIqkJCQkJqxCU3gsgJPgSiESGOVTFHWEsZVPv4cRGiKJtzbSatxJ7yd7LIwsnBQwtuTbDYcqsCUkqFAQU5HQZrMHdwCbmZ2StEm4G1XBHgDPtVVZPBqNgJteTakIFDICglhLyqTLMrYaAHq8qC6EDAY3RJWDuM42VtktQqygV2fgZ1LrKoLx6aQxvBQwBQL+/2Nzj2GZcNRZcR0odmc1yR6YLT6ppja6Gp0nBHZuN9QSVgpGE1HvtQgWAPFNj/avd+6K1CO87JbRZz9i9M7ylaDtLb9Zaq+zMucZ9t5Aa6Fh/9wsHiaQyxq4B8HY4W/DjAD4Nl+ZbALiVMQYA37LW/m8HZQHPI6y1ijH2CQD/Da5e5nPW2vsZYxcDuMtaeyOA3wbw/zDGfgvOCvzra1hZnoxuopCvoRnMzWEL1q/K2plJIUlk8b35ZuDuD23Gpj2zuOXqOTx4L4AnzwJggZM/5Q6wwiy/BOpOpZTl0J1KmFQzc8IFjqA+j52vaxmJpCYkJCSsIdiaw4K3CKraU6BeKFBTWI7mQT0Fxuc9iRBSNyrPlbP4ZhqmzKJziWDHtWWTkEs2X6CxEDuFtjkPk7r9mjul1ir3eau4I8alS83VpUS9UMBoEYi2uw5vMY3UUtqe9atw/FiVo2ClzCuusX04tp3GxJQQq39CaCjNg/paV41SqTrKamznzbMaeTY+t0lbSEWl2pksd/tned0ipzEmpfnG1TJK84Z0+ksg4kqEGAD6/RK9/iK4MBPJOp3DamdBjr8ckEUd1tWaK1Uc1WLu9vXb6+jvSOaq9eWCqmSUpMzBoZdNUFmWQxx97LI++1yw1p47YfOfHpSTrUD4ztObOtsuin7+LoC3HOp1rSjEftgtW4AdO1ztzAZgx3V7IUQrEM8VknTBBY6Dv3N2gFuunsOmPbPYdPJrgaet6xolAneI03z3BbHlev6J+TCbGluy55+YH58V3jn06vBpwIOfbavEqUP1Z0YiqQkJCQlrDEQUaX6zeraPcnd/4r58AhFZiqAybsGLGsbPZDrrryMXjBsYI4Nqa7SAyOsxJZbIc5zqS2A+RRaAO4e3/5oyQ71QhHoSIqikvnHBA0lqrssEggwgkCNS8ERc6eIzBHk3RVd20o7jXlNvEWZ+Ntj4YKKqluH9eF9KxM0MD4STEFttaUyYame6BHWphNvu3GjpSaCM0n27awfaM7B5ViPLa/T6pftiwZPvGEKbYG0mi29s8+WBRHNUozycxyiOWmdN0FNU90NzrG4/6mptLNxUQbMcu6+tK6idj+/35xISDhgGA0dQL7kE2LbN1c5EBGi11M50FdTTT3cW3xCShIbA3jo/wKZ3bZmsMK4guy+hm7I8e90sLnzrhZh/Yj68HpsZ7s6g3n4WcPuZjpADaT71ACCR1ISEhITVBMtcUq+fQwUiW2/NW0qmrjJHDkZF6PUE2umrRgsIT3xcQFBTJcInpLQCCETVnZu1iapy5JgIqiO7JqT0kuLKI5sxzbI26q8ISix1rCqvgjJhIYQC1wyis75JBM4lGLNg+Q2hRxE5l7ny7zvrL5G4OEiJ0ntjaN3U3dR1BiE1crRReYtt39fDGMPHbLRkvyW1c8rvSxZfIbWz6vo1d1Nuyc4bzul7T7O8DpbjSaDtZCEmFZWIfZegxver1y+R98tWpY+z6EZBTV5FpYofrTisJHWXQ0hAw6utHZu28n+7ca3NUtfxXLD731yTkHDgMBw6BXXbNmDHDsy/cueqrJ3Zp5Akv9/gxCFwxw5HUFfYHOokdGdT594/F2aGl/wCoTujeur1jqjedymw69sr0tK82pBIakJCQsIqgjUMZncBU8rO9iYdN4QklRK6kqgWCmedrKRTvQLZ8f2nggdiQqSAyXGFFYAjpJl2nall1pobJSIKIAQwuc+YdtVLZkKAkq1FCF2K1298LQmAMNvKhG1CnvL2et18qgYTDdmpRwW40FBVhmqhQDkqXHdonCQrNfJ+BaM5FnZNuyoUNMQKQGsGNdxfqnTRDXni3CDru+si4si5gRQGPT+Dqr0KHFJ3DQ+kEnB2WyKW9Fk6dl1nTVCVR0xOQx/qBMUxDkyKEau1vf5iIKC1J9/diiKZ18hyhd70CPlUGf6eVJXBaB5svu5vrn0fhTRBtY3haoMm/73FM8OTUp2fE02dTELCoYefQcXcHLbn89i86UJs/fhlwNw5wEa0bKQrlaDGNt9YQd20CfjCF4B/+sp2/Pa/3+x6UAdwCuPtZwG/8EHgNRc7orZC51CXwnOGKAHjNt71A+D489N86gFE6klNSEhIWE0wDHqUQy8ULhHXcJ+My0PwEIUkaZ9iS9ZJVWXgwoAJCyYsrGZOpSyJBMYzo74HNTPtUKZ+DVYo96CApqiXlUkdCCqT4x2qoVuVW/fIGvsn4IhvHI7kEonH51u5dFU57dlaG/ajh6oyVKMci3v6qBa9Gsv9Z6X2dtX6/2fv7eMku+py3+9a+6WqZiYhvDkkCCSBiMEIBNMoL5drnQiXHEETwCYIERHlOqJwTsA5DJygJscz3DHEE9Qz3BwFIVFjBwMGBQS0NScgODEJAQJcYhIBgQCSkMx0Ve2911r3j/Wy195VPdPT0z0zPbOfz6c+3VW1X1btmnTqqef5PU+wrSqVUJW2/1RVVj2NQ5J8UNJ41Gfv3k2M3Jxv5qyy4ZaVJKkKBNX2ilp7azxbOplkjEZ9CqfE+v7Ttl04XI8qYTLJ3H49RqMelZKBoPpe1jYqZfdrP+fVWr/uJLG1QP6ccc+qSDRZXpEPJoGgJu6W5s15VK3lVFWPR9zRas+ROOW0GWzl3wvboSqXVXb3B3neGE0AACAASURBVINN913NrUOHQ8aePcEfO3fKHPN37mTxqh2wZ0+Yg5w7Ze5Ir3K/iLtQFxfhuusgz+Guu+z9xz99joXXz3Pv7bb7lX+9FjBw6oX2/tZhPYe6QdAOUfK9tvuFn0/16vG9K9inw37RKakdOnTosIFgtAidoUAgp1B3iFaTLBDUYpTbmT43P2i3sx/APanz5MurZrqSyEpiZDSPKRPIFFQSKomZpOhJ2lBPwZJbSRlmTtskpU4ejpKJnYqqJ1lQy3Ql63TgSP31z4GzJ0fExb8ujbecJlQTq6KqSjrrrI4Ck6zN1KuAWjfrZaAZFARQuG1Hox5lkTEYTMiyshE0lCQalWgyysY8qkesooJN+vVKp9ZySh1NZliE/XaeJPvU3thO7H/3FTgeWV7aczu3dOrWniQ6KJ9ay2Bl9s9lWUmaleT9omGtji2+lf9iJDpfWaRhTWWZhZlnCshc3VBQTKM6HP9eqKq3rAq7EnSEs8MRw/ZlbKRz29i9AeZQobb5nn8+VJUlqB9wY5dWJB4yHC5w4U3zcPs2+Or1zaAkOOrtvjH8lwcv+sEX2UCraGYYWD5AyavF/74HnrSjqR53IUqrQkdSO3To0GEDom3HjWdQq1FuldOJVU/TvGwEC3lr7P7gq2DAzZpOUoSWCKeaape6ayYy6k3VIfwozJw6fuYJbeISd8P+pVWCPUGN7aWN4CNoEGkgBPjEgUr2AfvD25xjpFkZApdsFYrtWfXzqrMIanw+X+lSFrXyKSOS5+EJa5ug2nXWvaW9XovAuiTemGxOJlmjo7SNosxIY8uxO2dZZCgSquh1BAtxoqnc70laBzIZJRmPehhnZ/aPe7U53zRpBFSpIq3tvlHNj792nlh6cloUNjwpwRJwpRKyrMSkkpQyhFXFRN7O/DaTlQ8KHUntcJRgRTbSowCzulCf8AS45RY7WusfX1iwYvFwOIQzlglK2mDwIUpQByYtvGSBaz93Ldd/8frpACWYnk+9ad4SVa8edyFKq0Jn9+3QoUOHDYY2QY1nUD1BtcqgQEodCKolGiUyUc6mWZI4QjNVQ+MIYJzAa0qJnqSoUR5mYG2I07RaGm5ZHZokpLZqrLP46knqrMpJg6B6sikSXdt6XQiTh034rW/2MRnUvDCH60hTkloV1Sb4mlB5YhXDLMyrLqeg+seUs836MCRvkfUEVSR2nWlekUc2Yru/J1xZIJ5ZVrr3yFlllwk6mpXQ61+XqmSDiAKBfMbHstdAhZt9TJFlTaIcd78miaI3mFjy2yunemjr90KGLwTas6PeKj126rOd5bUWYn/NlSOm7aAlv+9k1GM86k+d+4AwVrVfza1Dh4PGrl3WAxtjcdE+ziptpEcAscUXbBfqrbfaoKTdu+vHh0MnFh9DVlc/Hxwr34v3LAaCuux8qieo3t58x06o9m64edyjCZ2S2uGYxr984Qsr3jY/6SR++Md+bB1X06HDWqD54TmeQdUqceFCXv2qIK+QiSbNS5K8sgSqN01MZdr8Pdk8Carn/qBGOaaSiFyHlF7Zq4KS6lOIQ3drmVjL8iQNtuV2OE+8DpmohqU3BDxB6EyFmqDaFNianHolzpNtXUVkKkrw9RbXWeTU2159zQxAnlX0+pMpBTWGiCzAXjW01tskKKM+eCkmk56A+m08cY2zjON9klSTxpU6WlJou9Z2xYyfaYU61bediuwtzdKR7WDVnVhVXocvE+o1exU1DktSlWwoqN46bIm9btTcSGcN91+/tPddbtb2wOiCk1YCIcSTgVOJPhcaY64/YgvaqGj3tETBSXEX59FaPbPSLtQb37mLkyZznP38YW11fdIOMJUlZMcIMVu18r01Upa3njv9fGf/XRE6ktrhmIbet48feeQjV7TtP3/72+u8mg4d1h5qkobgI2/hNUn9odz3hSZ5RdKz6mm6edI4hqniuU47L5gMCuQWt12ZBPUzhg1XUpTf3YwuUjuP6jpVyWxwEo4gCW1V2EAWi7TuY03q7WJC6u2+npDGRFoDRNt6ghqroZ6AJi1rc92/WROqOM1WqWkl0JOkSskoDEk16nygtvm2Z0jDGt0xtmxZCsdtq6RQE9QkVZbgVgmKJCigfm7Vk9MkUtf9bGqsrnoVFawNOA5M8mtWLfKZuwCoNK+scq6lVahdb2msqmotAukvxnnjWvrX6Qlqmlehp9YfuyxSEvy8cj0Te6gQWUb6qFMO+TjHMoQQ7wKeDHwe958WNnOqI6kHi5jdbdtmZUdHWPd8YtdRXz2z0i7UkyZzPPHb83BvNIN5x86amPrZzA1OUtvK9/DU4creq1hZ/tKVNu34Oe+v51M7+++K0JHUDh06dNhIMDSCksql3pQSKRIDapqgZpsmJJsnUXiRI5CtBF7Zq0hOGoFX2NIZSqG3t/Yqks0FepI6BbW0qb3RPiImMy65NybGjXM30nqtPbdNSONtlUprZU+LoHqGZUZEsk1e/euPCaq3ucbwBFVVCb2eJW591xE6tSYXzhRfY2tbtef0KqpXL0uymd2pbQQVsVVd6wmq73b18GqpR6y0Kuwa2lblsEaVhMqcJJpLNlpSle4Lkdaa/bmr0lqnvfLsMYugJqlTUt3r8N2pQDgG1OT+QNdoFkxZUn7j6we933GGHzPGPOlIL+KYwXBoCepll8Ell9jqmbubXZxHa/XMSrtQrYLqFNMztllCFiunGygoaTmsKkAJmiTUX4cbz7dE9Ymvm75WHZZFN5PaoUOHDhsJRgRSqiZp3Sm6n3qObNNkiqDG8PbcZFCEG/0K0a8gsyE74eahpL0BsleG/USvsr2pbl7V23tDgm+RBhXVz6H61wGQbipsxUyiGsop0Ej+bb9unyorUx9ipMh6lhTJVDnlr7bYxh2oHiLR5M7OGhMsXynTH4zZtHmJwZYReX9CFqmoVpWsGgS1jZhE+q5Ur6LGwUi+O7VSksnEWl0nE2vd9TbfQLgdefY/VZVQlBlKy8bNH8erqF4JbluVPZkMb7Pvro2uV1VmlJPMXXN705WtnBmPelPqsr+GaV6FGdz4WEZba28x7lEWKaO9g5l1M6uz+3YVNCvAPwohOpK6VlhctArqJZfA7t3MfSu1c41u/vRoq55pj9EOh9bie/XV8IpX2C5UT1wb47axpfWMbccc6fIBSheedSHz75sHCAFK+33/2iFKW4c27fhh59hrdfJ59XN37IIvXGF/ety72Lx/HKNTUjt06NBhg0FXMlhgVWFnUZO8CoQtntPMBhPSTQWyX4YZ0xB0VNpEXq9+Botu386u4ntOk2aqLm3ltlchNxcNEmv22mqUkOKrbcWMnykNNt4oMCndVJBsmkwFMclUY5QKfZmeoBolGrOoQWlLaVTuBKQKKkvEqtY5vLqnE4l0M5nxc57Q5f1JrQY6QloVKb1B00Lt0T6PVyjLslY686wKYUaTSRZIJ9jXUbhtl6tgSQFdOCVTJRRFGqy+qbfyaklRZuRZGQKf4utjlAyENWkR5vb7YdchMalsPVYjz8tG+JInqKEGKdTLuOofp2LH+0AzYVkto77vD74ntcN+8R4sUf0mMMEOvhtjzJOP7LI2IKIZVIZDGA4Zzs+zcNUOWz1zzjZ237z7qJpDbVt8r7jCzqA+97lNBfVT79rFntujyN97F+FL77Azl1/efUyopzFilTRUB52zbf8BSrD8nOn9n4FTL4J7roGHPhXOvBhECre+Ec6+3G7TWYEb6Ehqhw4dOqwhhBAnAX8InIX9jPwLwJeAP8cGk9wDzBtj7hNCCOBK4D8CS8DPG2NuWcl51KT+892ukxGJQaLIBhP6D90XCKrwc6WlJQkxQRUDp575GdFUWxXVhSGJRFsymUWP+wTgtKWyAmQKvbdX18yMp220nqgqPwe7aWKTgEurzuqWZVWmOgREWYJaq6hAg3TJqFYl2IFXMeMYd5+mkV01KLRSL2sp9vBhQr5PFSCjbKTVeruuv7Xtuh6eDCaRylw1iJydRdVKIBNDpRLSRJE4Aj9Yhkz799d/iRDUepUgK904X1na+VJPjNv2arsm2bh29e+OfNpXHdKVfVhTWWYukEo3ZoOlqzY6aBi64KQD413ARcBnqWdSO6wGe/bUbA+Cf3a4Zw/b5o6u6pmVhiQFAvv0OR5fuDlUsPZVDJz1Fnv/GAlLmoVDqg5q238f+lRLTO+7Db7xYUtQ79gJX/8QfHdPs2P2OA9Y6khqhw4dOqwtrgQ+Yox5iRAiBzYBbwb+1hjzNiHEm4A3Af8FOA84w91+FNjtfi4P0awASRw5EolGRfOU6aCgd+KI9IRRHWYUH0bakKOgnoIlmtnsz6ixkiqc9dcoCXtdum9YkNu/bKpesl/a7dy5jA9USl04Ur853ylSbTUdLOETiW7Mpfq5W09A07xq9HfGibVeafVQUeJtSKN1BK093+uDl0Ti7LxOQQ3BQakixZJisIpm3POpot/jNUkXvuSJqrfrevR6ZSCfsxRUVcnQsRpvp7QPPaoJe55XpImi158EK69SCakbcA11QxGZVNHjZZmhdVPZbKcgK6fEVhHRjWt5QsVOpArH6b3e/iylDiFLbcvvamZS7SI7knoAfMUYc8ORXsQxge0zyMRwyOKpsPt98wcfwLOOWGlIUqML1Sf3nvQUwDQJ1TESljQLqw5Qgmn775kXW4J6z9U2WOnMi6G831qBk031fp2qemRIqhDiPwO/iP2O87PAq4CTgWuBhwG3ABcZYwohRA94L/AjwL8DLzXG3HMk1t2hQ4cO+4MQ4kTgOcDPAxhjCqAQQvw08ONus/cAf48lqT8NvNcYY4BPCSFOEkKcbIz5xvIncYqiIwd+ftNDFRlJXtI7cUT+iAcDEQXCfKjIlFVPt0yrag0V1cEoaZXTTLuZU0c+Svu4maQ16WwfT2rIsNbivO5MNdLaSIXUiM2zq25mkcZZSKOaHVvB4whjHLwTVdLEPxuJvmo2oQSrAAppg5zSyCorAS1NTVKrhFJlwSLrrazxceLf41oan+IbwpAqHTpE20jSmvil2KAkT1B7WRn6U5NUk2clWV42VFTfJ1uvRQfLs0elZCDvbbW0nd7rr2WSKBKa9Uax0hx6UVXCZNQL5LSNMvrCxT/fTmleGY67+dLV4ItCiD8FPkj4aqiroDkU7PrELuZOmWN42jDMoO549g4qXQXr6JG2/K40JMm5li3iOdSzLmkS0mPM7utxyNVBbRX03kWroPpO2eyk6STgLmAJOALBSUKIRwOvA84xxpwFJMCFwP8D/K4x5gzgPuDVbpdXA/cZY54A/K7brkOHDh2ORpwOfBt4txDiViHEHwohNgNbPfF0P7/Pbf9o4KvR/l9zjzUghHiNEOJmIcTN39lrCZ0Iyb1lqHFJe6WtmOmV1sbryKnws6WenHp7b6YRLiBJbCkQWwoY1CTUJKa9FMj09ONlEm5mlGFGlmCE889QwERm5zk9aRVyOnBIpE1SEippEoVMrJ05H0wCSW2jbYPWK5hprOdP7S3PSwabRww2j+i5RN+aFE8HBEE9b1m6MKfCKYOzSFaWWwIZV8j4tF7/WGy1TVJNr1eSZ2WYY/UEdJO7DQaTMHvqt+v1ysbrko54epI8C7Hd1qjZlTChnsfNBttjtmaKW++rJ6iTcS/M5ravu19bTFBnvccrwUYOThJCPCCEeND9rKLfHxBCrIa1z8IAS06fB7zQ3V6wRsc+9tFOHgIblvTeF4YU3x3P3sHOm3YG4uqrZ470cmMF9alPbYYk/csHd1lCFeMLV9ibJ1jt549B+ACl4WlDdn3CBhrF79/i3Yvh8QMiVkeffKmt7rn1jfbnky+1yrQupgOW/L7HWaDSkbL7psBACFFirXDfAP4D8LPu+fcAv4m1vv20+x3gfcDvCyGEUx46dOjQ4WhCCjwN+DVjzKeFEFdirb3LYdYn4am/bcaYq4CrAJ72mIcYqPtMGwm4aW2vFKnGlIlVPv3JUl0fPKlVUZMYyAx48jmWCCUQKlqeV1ZLicDNpFZWfTTabhdXzYT7mbLryDSmjGYMeyVGJnUlTes7U5sOnDSClbwyGqt0+QljV8NjZ1QltpbGb6+VpHJJtF49tQm2VVAoFTZdtx3W45XFwQkj8n4R5lx9tQ+ATKxK6wlwVaQhDMgTtnbNSxuZSxT2c6mqSiDFqas2CCmRlpx6wueJbJpMd6TOwoGsskWRNRRTo2SDhItEk7jXZ5KmPVgnEpno8DpjBVw5NdaT0apIrXLsrL2efOZ5GVRZ7a5Zm6CuTkmFjfxpwRhzov9dCHGLMeZp8f01Oser1uI4xy3avtnFRYav2cnCVZcuG5Y0PO3I2X3j5QJcdx3kOdx1V62gLizAntvdDKpX875wRR3yc+bF9rFjeA7VIw5QmjtlLqio25+1vaGyrght66+p7PU00ReeMocTf7AZsHScWn8PO0k1xvybEOJy4CvACPgo8M/A/caEdylWE4LSYIyphBDfAx4OfCc+rhDiNcBrAB772Meu98vo0KFDh1n4GvA1Y8yn3f33YUnqvd7GK4Q4GfhWtP1jov2/H9h/qaP7wC0STdKbVvKSXtUgcWSqEWrkaafoV5i+CuRUb9LovkCUBpkZS1THjojG8PcdeTSVdCRUWSuxJ6peRS0TS1QnrsqklHZGtleFbQNR3U/FiCeonpTLRJFuKhCpVZOLBwdQ1MTU7iNdPUoyFWiUZiVCJkEdzPKKNHMznmUWrLL9zSPyTS7R1yfTKmFnYnFdtKlCVwnFOLcW1mjOFQhhSwAUWYOo5lHdS1Fk5EXGyFXJeKtv4jpF405UT06l1CExGJohRstZaWcpksvZf+Pj2rlga/H1ab1AmCH16ccAherVYUjReTxZtZU9ZSP5V2vJZNRrrCsmqssp1/uDSDPSR51y0PsdrRBCpNFnpdkS+MEfs491rf0QENK8jDG/sBbHP+YR+2a3bbP1MwsLDIdDti3ef1SFJUG93PPPh6qyBPUDH7DPxVybYasL9QtvrwkqWKJ1DM+hzoJXwVed1Ny2/sb3PRF9zvubXwr4gKVj/MuAWTjsJFUI8VCsOnoacD9wHTY8pA3/3edBKw3nnHPOBv7etEOHDhsVxphvCiG+KoR4ojHmS8C5wB3u9krgbe7nX7pdbgB+VQhxLTYw6Xv7nUcFEARrbzuECGxAke09LS0R3FI0Satf65YS+hqTGXtzXMZkAsZTm9f7KRuSZJSEcRoCkjxRnVruoAx/xLX7TC09uc4UHICgJr0KNUmRNBXU5WZVve3Uk1MPKS2ZJHfbVQkUQE6YjZWpRleSNCtJM0su803WUtx4TZHd2Sqqtiu0nJHI2yCo1IrkLEW18Tq0TemdlBmbItuubCuqLZU2ViDbZDTuP7XbN7criywolrP+zfjXUxVpIKhZVlLlaVBQY3gbsVeHK2UVZl+B49fenNPVU+uya9LhnAcDU5aU39j/f1IbCDcC7xNCfBj4P7B/V9YCVwNfBP4v4FLg5cAX1ujYxweGQ0tQL7vM9qMOh4cWtrPGiJN8/XKf8AS45ZawXCAOSXI7tmdQPUElev44I06HlPS7P+wvYGnrudPbHwfJvysiqUKIs4wxn1ujc/4EcLcx5tvu2NcDzwROir4hjNUErzR8TQiRAg8BvrtGa+nQoUOHtcavAX/ikn3vwgbDSWBBCPFqrIvkZ9y2H8LWz9yJraA5oO1OCIPsl415zjiESPYq5JaJJaj9ys6YMj1fak5Q6H79mJ1uMwhlA5HEWMIorUOT4n1d/YypZAhiMp6seuuvU1B9KJKoJMIT2SjIye4zTdiMI5gitUE87XoUDVRLOSLRlEs9ylGPYqmH1iKQ0+UqaaBOBZZahzRer7JKp1p6gioSg1Ei6p+tyW9VpMFKHIcC2fOXTv3UjfAjBSGkKa5aKYuMoqxtyWmiwi3Ly4Z6mgS7cW3xnkV+/fNJokKXax16ZNeZh8RfOZNsttVYfxz/unqDCVXhknqVVULHXlGOyCnYTtgsK8ndLHXbwttIGFZJsAKnrV7Xg4I+OuZL1wBvxCqePwzcDLxzjY77BGPMzwghftoY8x4XovQ3a3Ts4wOLi1ZBveQS2L2bxbNPYv7OnasP21ljzOpCvfVWG5S0e3cdjtQISQJLhHyozzHYhboarNuXD/sLWPKBSl5lPU7svytVUt/pPnD9MfCnxpj7D+GcXwF+TAixCWv3PRf7x3YReAk24betNLwS+Ef3/N9186gdOnQ4WmGMuQ04Z8ZTU1+Fur9lrz2oE0jT7DwFjEwwWiJ7JXJzYYnhoML0I7KRWMUUwGQG3ReYtP7wLiqQY4MoBYxdam+boDpiCoSkYKhnTxuvTQtkr7KKLSCURDri2txOBsLtZ1Y9aRWpQuYVJtVRZY3/3dpmVZFRjXLGe/tURYauZJ3A2ysjNU4FBTTuWC2Weshck2IVwji9Nx9MSPIqqLmqsB2txVJtSfWkOFZR/SxrkupgIQZrIfYKqEmaIUNlkYUb1L2pibPFeoLqu1a9YupVzyRRNh3Y2Z09uQNLPnuDCXm/sP9OKk1JbdFNc/8eJY153Zhwz3pt4EKdKkvIlbL2aaUSyjJrEFRVJSEkKosI6qxZWU/cPVn19T9ZbxVKKhw1IUirhRDibGxquAFuMsb8v2t8Cn9h7xdCnAV8E9vp3GEliMpEd+V7mDt7B3t2X8LCtstCuq8P39nz9T1HhKTOSvK9/PIZXahbd8HD55pE6Ek77Mykr585Dm2nHoec9LtStLtVtw7hxvOPu+TfFaX7GmOejbV/PAa4WQjxp0KI567mhG5W633YmpnPujVcha1juFgIcSd25vSP3C5/BDzcPX4x+w8h6dChQ4djG8LYmU5pkJsL5IljkpNGJCeOkCeOl61zWfHhSxeYVLXI6SS1yb2TFDNJ0ZM0qKdgiarws6b+1o+Cd3qVTRT291ONGJQkg8K9HqcCu9/byb7LoVzqMX5wk1VRI4KaDwrSvLQkM9xKl4ZsXEKw6zt150rzKhDU5VQ7q5YKinHuVNQszHLOInVtBJU0t2pibdW1+8Vpv1lesuWEJfJslQqiw3Kvpa2aeoIKdcVNO3X3QAFMSiVMxj0mE6sKe+LtE4Yzd30PFILUc6nNfl41XYXV12J1yb5HC7EVQvwn4N3AQ7EVfe8WQly8/70OGle5Uaz/ihUG7qBrUlg59uwJEuXcKXPM37mTuW2XMfxyFUiNT/WNQ3gOB9pJvtu21Um+s7pQeficJUfBSroD7thZE1c/g3qcIv6yYfHuxUZS80Gl/B4Ibevv1qFN/n3YOcdV8u+KZ1KNMV8WQvxXrOr5DuBsIYQA3nywXVrGmN8AfqP18F3A02dsO6a2xnXo0KHDcQ0hsDba1PWLphoqOyca+k1Hs/+0i1KEn3KJoKyG51Rk8x2ntXLqVFNTJlPWXIFTuwZlI6AJtx6hRG017leIsVubt6kOSmSmgvXXw2hvJ67VW1MlmCpxqmaGmqSUozyQRE9q0l5JNijqypq0TuP1UG7+0s9YeqIqpQnqq1YJQhmY2N+tUuv6ViuJxgUkacm4FfijtSSh2S+qo2snpQ0Z0loiVEKmbHBQju0nDZcwuqY+uTdzwpe3LCdKo7CKaXgsViF9IFREpv1zMbK8atQAJSlQNdfuFVRvX45DoooiCwS1LLKwj5S6YfONCaqeYfUOyqk7uZ8XXhXMhldSXw3Muc9CCCHeBuwBrliLgwshJPCAMeY+7Mzr6Wtx3OMK22vi2QjWmdvG7iPch9pO8n3HO2AwaCb5QmzzHdaK6RnbphW749zuG3/J0FZQDyrl90BYbs70/s/AqRcdN8m/K51JfTJ2VuongY8BLzTG3CKEOAVrw+0Knzt06NDhcECYQFDNltKm8zryGWpkEtOsjwFQoq6YUQJKgRjT3M7VypixVUvb5NS0ZlOtRTdBbJlYAproUIPT2M6dQ/Qqm3pXyrrSJtOWYI/TQEYllqRq0iZBVRI1Sakmdv6xHPWoJpYM2flRq56KxJDkZSCnU1U92LlWkwhkIpHSoLVAStOwCAOoIoXc/YwgU005ySjGebC5xtZZsEQuzZukDuokYU9wKZyKmbkZ1sge62293jKrqmRG76h086jTKm6SqAaxjNXemCzG6ca4YwmpLVGFQFZFmIfNKJ2119fKlGUWCGpRZqTu+F4ZltH18a+hTVKNkgy2jBprWTVB9cfc2CTVYPvkPRJmhEeu+uDGaCHErwLH3ifc9UQ7iQgs69uzh+H27esTrLMKtJN80xT++q/tc/Pz8Kl37eLxT5+bJp4nPaUOSzqOSelyOOSU34NF2/770KceF8m/K7L7Ar+Ptec+xRjzWmPMLQDGmK9j7SEdOnTo0OFwIQpEWhVKgRgniL0ZZm9e3yZpg6DqSYqeZO5naomqu4HtOpWRvZeB7V0l05i+shU3ZXOudbnUWFy36yx4gqor23mqiiyQRpkq8n5BPijIBgXpoKB3whKpm1/0VT3Lnddbfr0CO6uexc6i1iolWCXVWn4zJqMeSiXWwtsvGGwekeVVIIfhXFIHcnYgW/ByCDOeKgnzp/Z4K/vfuZRWWY2TcmfPhS5vx/Vr9wR1POqHGVSP1FmDN28e2TRfp6K2z+dfh7+lubX2pnl1yOQ0wIjV3Y4O/C/gU0KIS4UQlwKfph6HWit8TAjxRiHEY4QQD/O3NT7HsQUvUXovrR/snJubCtZZvHvxsC4ttvhCneS7tASvf32tmi4swJ67Insv2J83ng/fvbkOS7r38K5/oyBO+d12zrb1/TJiVvLvqa+wyb/HsPV3pXbf/wiMjDEKgj2kb4xZMsZcvW6r69ChQ4cOUxBbCkxfI8YSQ61Uht4uJaB0VTFxqq+3+yphyWQ1ndyLkoGgGi1DhynQCC+SrdnT0LnqVV0f2uTW0lh/v7J24hhOWRXQmHVtQ6YaoxSaBIkiH1gy5QOOkrwMMhk3BAAAIABJREFU/bFqYoOQfBqyWuo1qmtEYkA1q068LThO8G3X2lRFymjfgKW9mwJJ3PKQveT9gt5gYq3DS9RKqUObtIJVNz1BC69RahRWPfVKow8h8r+TMkV4fS9qbPVVKkFWdchRbOf19t+gtLYU1iRRKJrpy1WRkqSaskgbCcGqSkj8bO9gQqUkaWI7XOME4lmEOE5uzvtFsG1XRYpGhtTl1cKsEdc9EjDG/J4Q4kZs5QzAy40xn1nj0/g+1DjEzdBZf5fHMt2oi6dyeIJ19oODSfKd6kL90pWAqFNktw6P+7Ck5XBYK4aWS/49xq2/KyWpH8dWx+x19zcBH8VWx3To0KFDh8MFaTB9HZJ7xTgiXUo0SWEpEehAVKcswG0oiRk1ldMYvhZG5jNqbjwp9Qm6rn9VKoGISaonapm261TSqqiJrj2MmbLZ7/68EbGUiUIn0tqAEhozp+mmIiQCAyF8yfeyyn6JHtv0XJFoUEkgpe30X5kodKRU6iqxNy0DQS0Lm5A72DJisHlEmlf0T6gXXiz18Hqkt/02rqcjoVVkJY57TieTLMykeoKqtWzM/i7XiRpDaxnsvFO2Z5euPAt+W3eFAmHWRZ1MrJ2y66tx/KcKn0ScReFQs2CJrrUrD7aMyHplCLNK3dpn9c+uFCLLyB79qFXvf6QhhHgctlP+g/Fjxph/XatzGGNOW6tjHVdwSUS7/u4y5rZdxHA4ZM8ndoW5xF2f2MX2Z20/7Km+B0ryvfGduzhpMsfZz4/mTE8+z9p7t54LZ72lOYPqw5I6khoQp/zu+foedjx7R+PLCJ/ovC5BWceR9XelJLVvjPEEFWPMXlch0+EYwWc/9SmK+1fWLHT3F77Ajzzykeu8og4dOsyEdGplP3rMk8CsRQa88skyNlrlSKK/P8owWliSWqSYKmmQvvQhIxti4wgqmZ1BNYlpzrw6iNI+Zk6omiouwITmuf3vvr/U2XWNrGdSJdgwpkQH9c0TqVl23rZ92JOxer/SBihhCakkCvSJCaqStmpGS0YP1gpqlpds2rIUCGqjl1WamuTNsPYaLYOK6s8Rb1e17LtpoqmUbCiRWtv7sf02hk/r9eR0uWTe+MuImLB6BTN+3p+j8cVBtE/j9+g9WdbmjU0ZzvsTu04/q1olaFdxFDphV2H/1UVJ8bV7D3q/owgfxBkMgM3A44D/D3jSWp1ACPGiGQ9/D/isMeZba3WeYw6uG3Vu20XMF9ew8P6nsv2C7Q0CA9YWerjnUn2S72WXWQU1TvI9aTLHE789bxXUrUP4whVWjTv1Ikty2jjOw5Jmwaf8xqFJO569gz1f3xPur1mIUhuzrL/33Watv1unmu6ipObDmyy9FlgpSd0nhHian0UVQvwIje+5O2x0FPffv2Li+eWbb17n1XTo0GFZSBeKNJZQ2uRc4ccL29bdFoKiOk4sgY0stzVBzQJBBVcVk2lkr7TKaartTCz7Jx4+SdhkxtqAPal+MGkquokOFTf+fOAqbaRG9HzoT4JIbDWNdEG6omVf1U6RXI502X0UUkmM8oTUElUUVEVTsfMktSpSiqUeVZlRjO3Js7ykN5gw2Dwi3zQJoUtQz7nGymKDgLZSdsP5ImtvnOqbRHU8lZLhvqoSlMvU0WmdpAuW+HmC6tdXn8d2uyaJonJk2Z7H2Z5THQhqVdqAJH9t2rOv+1NwY3iiqVuE16cP+xlUmSqboOx/UpPTOJRq5Th66mRWA2PMk+P7Qog54OfX+DSvBp6B7asH+HHgU8APCCEu7ca6ZiAqFx0Ohyy8/6nMf/qNbLv/NnZ/58NHJNE3znJy/JmLLoLrr4dXvarOeDr7+ZHF9+TzLEE9+/Jpu2hHTJdFrJC2Q5TW3d69nPX3rEusXfvGC2q79ga3/66UpP4n4DohxNfd/ZOBl67Pkjp06NChw3IwCZjNypJUD2+djdGa4zOJI4sAYxozobbqRYSQJA9LCCsbkLS5sAm+XkFdRr31tTaiFJjMoPtNdTUpNexzhC3R9f7RekXqUoMzbzSdhifOUM+wikw31y8twW7sl2qr4kJUS1NilLBzp85a6itpdJVQLPWYjHo2HEhJei4IqDeYkG+aNBXUFmlrE1Xbszr7y4R47tST1GCjhcb/sf3cqt/ez4DKXIcUXU9Q80GzO7eaZMto683ApKrMGI96VC7Fd7ntZkEmvmNVT32ZEc+++tqbrFerqJ6oxhU2PtxqNdjIJLUNY8weIcQ71/iwGjjTGHMvgBBiK7Ab+FFsLU1HUtuIulEBhhdczLb7b+Oyr1x9xBJ9/Szqjh2wc2f989JL92Pxvedqq6CeeXH9eGfvPWjEIUqH9f1vf6mwdWiDr268AJ74uukKoQ2GFZFU90fxB4EnYm0nXzTGrLZZu0OHDh06rBLCcb5G/6hHJUNvKlDPf/rZVLeZmaRBufT3tSNnQmpEbitugj1WmjA36o+3orWWApEZzH6CbEWvgkSGeVhSbQOaMgUx4fRqrlMEjZ5NPGYR0xhmmRCexBGgEJBUpEhpqIrUqomlvT69wYSsRZZ0Nf0CQ5+qV0ddiFJZZo2gIG+bLcuMokyRUk/NncYINmB3abzlN000SapIEkWel2RemZTNfyPeutx47alGVZKySMN7rlTiFNQ0pAh7Au7J5yz47XqDSW3TbVmUw+uO+1Iricxjq69s7BNbsQ8WG52kCiEehSWMBtuRer4QQhhj1qqK5lRPUB2+BfyAMea7Qojus94sbG+qWYt3L7L7Ox8+PCE6y8DPor7gBfDiF1uC6nn0rR85gMX33sWuC/UQsHj3Im//x7dz0ZMvarz/6zqbCtPW361DeM4H4HO/fUxUCB1MZN4c8GTgbOBlQoifW58ldejQoUOHZWGAFkFtkNWqqbCavvtwX8pG7QxlYntQJ2kjTVf2KtITxyQnjkhOGiG3TCxhbCuelZyegfXrCYqtRC5J5FiEG2MJo9RajV1djQ1eqiBTDRIpIoKVDArk5gKxZYLYMkGeOG48H/ZxZNbf2nZfUyWNtFhdSWSqXTJwRT6Y1JfSkzRvh0183c0kzHn62UmthbUFj3J7G+c2nbeSFF6NrCRGSYoiC8cuiozxqM9oZG3EqUvA9fdVi7ABFGXKZGJJrd8ny8sQVFTXuFh+EdKJJxnFUm8mqfZW32KcMxn1ws92MFNMUEWiSRLdUFVloi1JzixRblfxKJWEsKTwnrhKncrZiv11a75Pq6vssSdY5e0ogBDiQmwf/Yvc7VPAs9eQoAL8byHEXwkhXimEeCVwA3CjEGIzNrSpw34Qz6BeOrw0WD8PV/VMXDkzHMIb3gBXXw1PeUrT4rvpec7i+8mfs0E7Z18Oz3yvJTlxDU2Hg4J//y8bXsaH7/xwCFG64h+vYP5988ydMrd+J3/S9tkk9P7P2PnUL13ZfF83WD3NipRUIcTVwOOB2yB8nWmA967Tujp06NChwywY6poXaNa+UCtzolfVFl+lYWQrQxjXHahhH5fwKjJl5063FFbpSzWikph9ebhPJWvFdtbysvqzs1ACM5YIFT02TjD78uYacfOtvaoxm2oAJqmtuxmU1m4cKZBiUNbHyhSU9meDvLrHQ8dr3HUaCJiz9iYSkRjSvLJkTtdhRn5u0lfMaBlVxFQEYhXDq6djRzgBiiKjLDJ0Uiuok4ntGM3ykiRVoQvVW3jBHt8TVj+LmqSKPKtcgq5yFts6IMmrqJ7gxSnCs+BV3LLMgoLatvW27bsi0Xj66K9VmttrFScVJymB7Mfn818iGG210vY5/TWVM9KRVwSz4ZXUNwM/Yoz5LoDrL/174M/W8ByvxRLgZ2Pdcu8B/sIR4Y0rw6wzdn1iF3OnzDVCdLxydjgTfePKGYB3vAPefMEubv/aHIuLw0BUATjpKZ3Fd40Rv/9nP+psXvBnL+BZj3kWb118Kx982QcPj6Lq0Z5BvfECa/99zgfs/Q02n7rSmdRzgCet8Td3HTp06HDEIYQ4B9tBeAo2EO5zwMf9h8KjDqZOyTWJJaihJ1W53lPXgSqUI3qlDATVz5+2w4UaBNXPnXr0q5qUxnbiGC7J1wcm+SocUQKqrsEJNuN+a74wtaqqULqppvqwpvgStMN7elaFDeQ08+pxgpCm7o+VmmTzBP29AUBTqYt7Ub1V1z3vZyGzXtmwnqZ52VD44rlNf329ZTZcJq8UOrJZKTn1WFiHllTU86Y+3VfmOqinnpyCVTljAhnben2/q1IJmZvlVSpBSD2V3uu3y/OyoXh6ghqTT/B1PnY7bzXe38yqSLw9uXnu8HuiGuFN8Ws4WBg2PEkFm7TrsebKpjHGCCFuAgrsJfun7vNeC3EqkcPct1LmP/5CFn6uJiKNjtTDXDlz/vlQVZCm8JLXzPEb988z/+YF+O9Dhk9atGQFYS2gX97dWXzXCO0QpRef+WKuvv1qLnryRVP/LtYdbfvvc95viernftuqqxtsPnWlJPVzwKOAb6zjWjp06NDhsEEI8fPA64C7gX8GvoTNoH028F+EEJ8DLjHGfOWILXIWjKtxaXWgBihLUo2SiFFquyvUDFIZIZA8R1J9ByulQKARzCCUnkBkuibJXjVNTKO/lVEaiKYpZUMRjZN+G12puJCjePZxnGJa5FZIg9gysaTakfCATFlbc/Q6jbb23rKoZ0N1VKuiWmpj4ghTqEbREl0lLr3XNFJoPXnz9TK2A9XaeT1Kl5Lr1VKvjlYqsRQBa/GVUodZ00rJQGYHg0mjg7ReZ3PusyozZOFnPGVYFxB6R2Oy6dfr4cOhtJZMRr0GQW3vGyPNa4u1THXjepRlRpatfMTRE1Wf7tuepV0pzCpF2KMEfw18RAjhldOXAx9ayxMIIeaB38EqtAL4PSHErxtj3reW59nQiOVKF587fM1OFq66NKS67r559xFJ9QW7pLk5+Nu/hUsuqRN8F5jn5i9tg+9cCYg69XXrsEvxXQcs3r3Ih+/8MBc9+SKuuf0aPv+tz3PnfXfygZd+IPy7WFdVtZ38u3VoA5R8B24bR3k9zUpJ6iOAO4QQ/0TIRQRjzE+ty6o6dOjQYf2xGXiWMWZmnZYQ4qnAGcDRRVLBqqVuJtQT1lloKKsRfL2L0dLObXorba/CbFaYrFZEDa5nNT6HnyVtrUmUYFBWVfVr9OvYH1GOj51oAhVxZLZBstuvx83LikTD5gL25VNEVWhRW4FRNhV4qRfW5o+mItVTpgqpvZLqqmWkCfUttgdVga9LKSI10hE+pZJg7233nmotI8Ka1D8La4ONa2cA9u3bxObNSw2CGltv42ukVIKsNMWoF97nptVWBnXY19D4YCh/nCRRdpa0soTV23DjEKTYmpsMLGnN+0VQauPjay3JsrIxn7o/eOux/4JApqsLTpJZRu/7H7WqfY8GGGN2CCFeCPyf7qErjTE3rPFp3gLM+U5UIcQjgY8DHUn18HLl/LwtIN29O9TPbFu8/7CnuraF3cVFeOZDd/GUl8+xe7e1+A6HQ/LHnscz9WXwsHPhrLc0ldPO4rumaCvpAFfffjW9pMe1n7s2bOe3OSwW4HsXrWq+QetpVkpSf3M9F9GhQ4cOhxvGmD84wPO3Ha61HBQSgzmhskplTO6q6L4ncuPl/8TLnus63TJB9CsYWILaPldj/hWsSuvV23j+FKxiOqYmkbHi2ibSUfJw+/gxAvmKK29a+8TzsqJNVMsE42ZwhSOd1b5e2E8VGRJr9417UqU0pHk51Z0KTk3VAlxoUlVkU2qlDwjS8QxspFRa4lorkWmUhOtnTb2CqrVkMBjb43qVVdn507ZKHtRhNyvr8xHLMmuk7VZFCrm12Ja+X3aZLxLqGVdr0a0twk1V1HfGxigq3ytbTc2ktu+DDXCSqSbNq3Btff3Mauy+uqwYf+3eA294lEII8WTgk8aYDwohTgJOF0JIY9ZUH5aeoDr8OwcXrHl8YDi0BPWyy6xcObRWzt037z7sqb7tOdQLLoBnnzHH9W+Y5xWvWOB580M++a4rOOPBKMG3jc7iu6ZozyZ7RfW6z1/Hn3z2T7jms9eQypQLf+hCbv3mrVyyeAmXDS8DanXVzzivCXE9BuppVlpB8w9CiMcBZxhjPi6E2AQc/P8tOnTo0OEogxDiNODXgFOJ/iYerU4RI0GfoJCJgbFBjKM/xUpiKolo7+TIh6mktb9mylppvXraV5gTFLpvEArkUqxEGjtfCjVBzUxtCY4gvCbd6lAVEGZjgWZvZkRUAknyRLSlmgo/G5tIjLP2NuZV/e/9CtGavzVaYiYSPc5QLeKpXKqsh+/p9ERVVwlpXtXKKVAs9YIdtSrrfWPbrJ/nzPISiowizKnasCSvKs4iqFlekmjJZJI11Eu/v0dtv623ade7+O3aoUTF2IZOxa89nkGNSWRMUJNEobBhSGBnfQcnLJEPijAfWxWps0XbOWOANG+qunEPqoeQruO1V9pUYnft07xCL1N7cyBs8JnUdwM/7j537cGOJ/wb8Ko1PMdHhBB/Qx3G9FLW2FJ8TGBxEXbvZtdbz2XuL6+Es09i/s6dYdbw3r33Tilp6wUv7N74zl08kM1hzJA37BqSP2mBs2+8gH/5g9M54YHb4GmX24CkNmHpsObwxLKtqL7qqa/iJ//0JxlVIzZlmxhVI9740Tfyy+f8Mjtv2gnAzpt2hlTgNZtdPQbqaVaa7vtLwGuAh2FTfh8NvBOYYXDu0KFDhw2FDwB/BHwQWEt1Yn0gwCQ2RVeoZnJvIKGerPkP9fFzfvbUET7TV9DX6L5BDwSiAtBNotpGYhopvoBVUL1yms6wA+8HsYIX7L0xUXWvoXFcF7KEV1HdYwCi8oFRVkU17hhqX49yqU7aBZvsWxa96L62XZ/eXlolVHr2tShGvalZzvg1tWc2pdSBoCotSdDkmU309fOnPgzJo9crw76B/Lo5Vh0ptcpVv3golUyR2zZ8SFIbPiHYk2g/Y+rnc4FGsFGaV+SDws6jRjPSlVt3/Hr8tYyTk/3rS1JN1ivDsfyscJpXIYH5oLHx032lMeZBIcQFwIeMMa8XQty+licwxvy6EOLFwLOw3yldZYx5/1qeY8NjcTFIl3OnwnzvAl70p29m4Wf/O1BbOC8868LDluo7HMI9n57jBSfNc9ZZ1nrMvYAuOFHdCqd1Cb5HArGi6pEnOc98zDP55Fc/GUKVrrvjOs57wnm84aNv4LmnP5edN+2cSok+JEV1uTnTuJ4mVtOPwvnUldp9Xws8Hfg0gDHmy0KI71u3VXXo0KHD4cPYGPOOI72IFcOAHAtEKazld5RivK3XkdCp5NygUFoFVWwpIrLnKmAUiApEaSxB9eppKWyvaaxYKoFckoGoilLY7eIOVQ/XDUrp5lJXoIZNzaGC3a99/CiAya61noGdUlF9lUmimkm+7veGLbeRsCvqn87eC5bgxbOnbShHIJWqe059gq8nnr3+hF6vDETOz5kWRRbWIyOC6MlelpX2/DMqYkQrQCmGcfZhv81k1CTs/lh5f9JQTQMxbZFeXwvj51u1SkLysVYyqKpTtTK6nnuNyXCalfS3jOoZ4ESRD9TM92ilWM90XyHEu4AXAN8yxpzlHnsY8OdYZ8Y9wLwx5r5DOI0RQjwFq5z6EYU1T941xvwF8BcHs48Q4vnAlVhn3R8aY942Y5t57MiYAT5jjPnZQ1/tEcCePSE0aQgsvOz9zHMBW+/4ELvv3NkgJetJUONZ1MVF2P72Id98+QK/dOY899ywjVMnV4LM4cw3dgm+RwgxsfSq6vtfar/zueDPLwDghi/dwE898ae4+var+eHv+2E+dtfH1j8NeAPW06yUpE6MMYUQrvpAiJSjpuq6Q4cOHQ4JVwohfgP4KM1guFuO3JKWh6gE8v7U1spM0jrRNlZJY3URF37kiKzYXFj1tAW5JBGljUkS+5JawXQkM3zMT3wwkrDqqV/X3toy2ghK8ussExvQpNzxokobn0AsDkBgzThF9Kvmtu16mkmK2ZtjJil6kqEnzvbrE3h7FXqpTvM1jox7BTXUz7iAJE+oqiJrqJn7Xacjp56gqiqhKFNKd4wsLwM5zfKSPC9J8zIQtryasPeBLWFd4dJHRC/DElVv/ZVRiJLfzpPDkOAbqbueQLdJbm8waYQq+eAi/5i/Nj44qkHo90Mi4xRhqAlvfK7+ljH5oJ5pFYlBJgqhzFTq8sFgHZXUPwZ+n2Zn/JuAvzXGvE0I8SZ3/78cwjnehLX83gJ8VAhxIvCuQzhegBDiJmPMs4UQD9L8TCewzTQn7mffBEuanwt8DdgjhLjBGHNHtM0ZwA5sQN19G1rc2N5Ul4anDdn2jNfZwKRnHr7ApBefuYs3vHmOW39myM6dsGMH3Hgd/OxTnsKpey9DsYnk3L/qEnyPEnhVFWiQ1Sv+8Qquuf0annv6c/n4XR/nuac/d/3TgDdgPc1Kv5b8ByHEm4GBEOK5wHVYa1yHDh06bHT8MPBLwNuAt7vb5Ud0RfuDFpaEuZvelwcCKLwd9kB228zUoUeRPVOUAvFggrmvj/le3xI+VauTtns1qowZJ4i92TRB9TbdcYrZ20Pv7aFGbq0HA58iHN1mElT/fOXOGRFUPc7Qk6yhzMpEoSuJVklDVQVHTlsE1WOWNRZqlTNJlCOozoobEVSws6mDwYSBq3fpD8aBoGZu7jLNSnqDCZu2LNEfTMgdic2dytq81efNnU24XfHiA5w8cdauyqYss5kqcN+RxFkEVaYqhBilvRKZaPszSt6NLcizrluS6vBae4MJWc++3v6WMf0TlkjyCpEYRGJI8hKR6NVbfQHrj1/l7QAwxtwItPuUfxp4j/v9PcD5h7B4jDEfNcY8zRjzi8biAWPMlYdyzOjYz3Y/TzDGnBjdTtgfQXV4OnCnMeYuY0wBXIt97TF+CfgDryS3wpk2NNqBSYt3Lx6W8z7+6XMsvH6ej12zyHnnwY3XLXLd687ncVtu5rv5uZQq+hsVW3w7HBFsf9Z2hqcNpyzAn/q3T/HL5/wyn/zqJ7n8eZdz6zdv5SdO/wlu+eYtTKr6izqvqs6dMnfoi3nS9iYJ9fU09/4tnHxe87l7F+GOXYd+zkPESr+afBPwauCzwP+NHaj/w/VaVIcOHTocRlwAnO4+aB39UMKm9ibaktIy6gktHYHbD0E1SoYOU5MYGMva8juWjvz2rCrrzhP2La2iKtzs6VQ1TFijO/7IEkWwc42mTGzYUSkhaQYmtVXUxn1PRttpsC37r9mXY0ZZWKupJGoynVxbOdJqlAiESisZwn6CPbeqSZxP7fUhQMbVtrRrWcIlUAmqSkL1jJS6oZ5acmnVTzlj5jPvT/9z9AFHPtzIq6hZRHSF1CEQKX7dvgpnFrxa2xtMpp9rVb/oKiHtNYmwV6FnwRNUf/3SrAwhVFLaf3dpryQbFCQhMCmhXOrZ5GVnz55FflcEA2aV/aqrxFZjzDcAjDHfOFT10FmKp16AMeZVQojfMsb8xiEeXwK3e7vyQeDRwFej+18DfrS1zQ+4c3wCawn+TWPMR1a71qMF7WCc4anDwxaYxNYh+XCB9xXzvP2Gbfz6r1xJltr+04e1a0W8mnqUqWPHI2IV1BPWPV/fwwdf9sHwb+ati28NacAX/PkFvO5HX7e+3bu+nubUi+Cea+ChT50O2TrCWGm6rwb+l7t16NChw7GEzwAnARvnW35P4BJtK2T8jGnUTbosojAi4bmGs+2aSYq5b2DnWj3aFSfjdGZH6tR2lQwEFWxHqT9HA7PqajwiRdgkxhLE1muM511rguqIZeVrYBK0I9VeOTXKVccob+dNQ5WMR1VmM4ORpNShVzSeqQyXTDVV2Dyr6A/GgQzmTiFsW2398bSWVh2NiKqJbMdAw+ab5pVVJ7MSmdoE3fGoFwiqUnKKoLZnXdO8CipqczvTSNqNSWtsRfbP64gQehLqYWdOx0GN9cgGE7JNE0vYU42oNGqSBqVbFWnDin2wOAS77yOEEDdH968yxly12oOtEn+1n+f+4VAPbozRQojPCCEea4z5ykHsOuuitsfAUmzX9I8D3w/8byHEWcaY+xsHEuI12HBOHvvYxx7EEtYZ0QDork/sYu6UOYb3wJ5P/g4LP2s/wO/6xC62P2t7IB3rQSb+/n/u4iGnz3H28539844h9958Hm990WX8w5fO5cRnvIWzu/7TDQNPWGM7786bdgbC6tOA17V7t/1lxkOfCre+Ee67zdYVHSXW35Wm+97NjBlUY8zpa76iDh06dDi82Ap8UQixh+ZM6lFZQeMhfDiST+jNDHgi14ZTFf0cqCeKsVpplJwiqKZNGv1caQt+5hQIz4dzuOMJR1iMFvVzqQbluk3dawGmEoJNMjsCwSgZZnLNJA3nsOeRDYKqiiyQHY+YWOkqoZxYIufnOFVl1dLl+kOXm6H1s6cy12QwpZzG6mub4MaIZ0uFrBVXpRKkO06aV3X/aXQsXxNjd5D1Nq015y0V1h8z2Hpdsq5WSaiAiZVTGY7bVKKl1GjszKmffc03TUh7ZSMFWCYK4RXl1P3019YR1MrdVgORp/Qfs2ox8zvGmHMOcp97hRAnOxX1ZA7xyy9jzPXtx4QQv+ae+7tDOXaEk4HPCyH+CdgXnXt/fwO/Bjwmuv/9wNdnbPMpY0wJ3C2E+BKWtDb8p474XwVwzjnnHD15J1EZ6dypc8z/2QUsLBi2/+4HWIRGuM3wtPXrR33I6XM85ivz3PqRBe7vDfnY713Bf3vRNXyjfxHPOPPDzP8WvL5nw5SATj3dYNhfGvC6de+251PPvNgS1HuuPqqqaVb6Vz/+I90HfgZbR9OhQ4cOGx2HZJc7EhD9CgaWpDb6SjNjQ5KiuVGvOoagoijUyLQraiKCGgitJ6plYm2TVUutdY979VI4Jc2UCSJTDeIYji1NvR6/hrY668+R6fr1OBU1kEZHvvXeXm3HlRo9SUPdjFfkylEoTtfzAAAgAElEQVRONclC6A80w5F0JUOHp0ntz2LcQylJkuhGf2iAJ39RmLLfrj8Yh/0aRDLqG41h3Hyo1LphMw7POcJcFNZiHFtzlUqQlW6owFleQWGfi1VLD0+YM2e/Dcm+m+x8KEASVb8kVEHV9GgTfg8pdbDzai0YnLBkj92qqZGJIunZtdkvEdyxKkmxd0BVpFOk+mBhiorRV759SMc4SNwAvBI74/5K4C8P5WBCiF/FznbGM6KnCCEuBv7HGs2n/tYq9tkDnOF6pv8NuBBoJ/d+AHgZ8MdCiEdg7b93HcpCDyt8Gen8PMNt21i4wTA/L9jGIrvft442zBbOfv6QWz+ywGO+Ms+3v3Ee/+1F1/AvD7mcM15orZkLzHP97QsRS+2wkTArDfilP/RSLjzrQqD5ZciaBCjBdM3MvYtWQT3rEmsBPkq+6Fip3fffWw/9DyHETcBb135JHTp06LD+EEIIF0SyrGXOb3M413VACAODypLTWQpjYjBjiRgnTUKX6JqMQlA+A1w9DTQVVJHqUOcS9nNk1pNDIa3F1JQSJiki08heiehF5MiTXHcuWjOnsVoqiKy/0ayrVWwjku0UVBNZa9UoR+3rMXlggCqyYOstRvlUGJIndUolgQhqLdGFT+eVtfoJM4lqVWRTimp/MGawedR4LO4c9WolEAhy5apyvJoLtcW3LFKUSpiMeiSJYrB51CCWk1GvEYLkCbCUOjwWE1WjZB3W5CzCnlj2t4zJt4zq2pm4OkYlyETV9ttoTtRbcT05lalCJjqQTO3OmcQ2300Tm7ZcydBfq4qMcpRTjPIpct22D68Y6/RfsBDiz7BW1kcIIb6G/cLrbcCCEOLVwFewX+ofCl4LnAc84O4b4O+BIbB0iMe2B9zP38D97FM5Av032HnTdxljPi+EuBS42Rhzg3vueUKIOwAF/PqMz5NHN4ZD2LYNLruM4SWXsO0ZrK8NE2xgzcPnGiTh7LPhX776FJ53xtXc9r2LeOor6v7TfLjAhf++B/tPosNGRjsNeOElCyy8ZIFrP3ct13/x+rWvpYFp6+/43rqa5gh3qK7U7vu06K7EKqsnrMuKOnTo0OHwYFEI8RfAX8azWEKIHHg2VgVZxNZMHD3INGazwmTG3hLbcQq2Ribejko2A408yqQONHJqp0h1II7CEdoYQppGAI2ZpKhRbglqFBRvtETKaqaC6o8jolTe/QY9eduvJ9vjNMydkqmQ4uvhFdTJAwOKvYN63nSShVlOXUmqcnaAUIzY4uuts6qo7cD2Z23f9WQwSTRpXquTszpU5X4svkCDdJduVrZw5+4NbIdpe57VE20RzZqST3el2idVIKje0gs2wGgWQY17a0WiA9GMZ3r3h5jozrJI+/OpImN0/2bGe/v2cWkOXUk161dBY4x52TJPnbuGp/m6Meae+AEhxHeMMe1U4VVDCPFjwO8BZwI5lnTuO1DCrzHmQ9ggzfixt0a/G+Bid9uYWFyE3bvhkktYvOFKdvdFSPRdFxsmWIIak4Z7Fyn/7nweIQV//51LOKu/m1s/shhmVI8W1avDoSNWSRdessD8++bZds62QFDX5d9b2/r7uAvhX/8c7rk2/Ps7UkFKK7X7vj36vcIVVK/5ajp06NDh8OH5wC8Af+Ysa/djxxkSbGfq7xpjbjuC65sJk4De5NSpvsCkIJzgJJfi7UxdSQOWrCa2k1Q79dETmqk5U7/PDKLqFVczcYprapU72asQmQo2X3vO2LIqmsTVE1RnW25YlGE6SMlZe4O12L0GXzMDUC3laJVQjXIKp8yBDUUKCbwp4F5u6upaPMFLUh2su3l/gtaSqrAE16uobYKaZSVJqq3dVktwgUeZS/LFWVg9EYuVUg8//+rJZ9VSUJWzFad5ZdXRSiJk1E+q5UzSCtb2q6ppIilctUyaV+SbJtZ666plPCn1yml7JjeJFHIfRKUTHezUs+CJqiqadUV+/2qUM35wE+O9fZRK6A0m1o4dBSatbi5VrGdP6rpBCPEQY8z3jDHnth7fBPzJGp/u97F23euwIsTPYWdHj28sLoaZ1MVTYb7/DhYWDMNnDBm+ZB0TfV340dJH5/nWQ7bx6H1XsrQkuOvR78f0hrznI0NeiZ1RDUS1wzGH4WlDtp2zbf2V+7Y6unVoO1RvmofBVmv/PUJBSiu1+3b/FXTo0OFYw/XAa40x/1MIkQGPAEbt5MmjDboH45MT8vs0aiCaSuomjXwwqTtQlU1KNUrW6b8tiF4F/ap+zpPHRNskX09QfACSU1xjwikyjdwyCdbgtpW4QVCd1dcTVNPXUIppkgp1kBJYq7JTTo1bqy5Sqn22qsSHIwEUo16oYfE2Vg+JCjOfsaLpt0jzpnInpXazqfVriglqbzBBpjootEJqMhcOVM++ykaarq4k0p3Hz8L6OVWZaoTSVI6gmkCONf3BJFirqyJthC7NqsEBZy9OCSTYw67REtQkr0L9iw8w0gDtDtlK1tt4lVW597VI7XV2QVTtf2kiMRglXO2Psw0rOy9slGgQ1JhwrzbRN8ZGJKnAPwFP9HeEEE/HVgH+B9ahp94Yc6cQIjHGKODdQohPrvU5Nhz27GHX21/E3KnOhvmy9zN8Bix+6lr25I9f20TftsV365DvbT6PU/dexhfuO5fx49/C/b2h48xDvjpZ4Ht3dRbfYxntLt51U+5nYesQztgGn7vsiAYprdTuu1+rhjHmirVZTocOHTocNvwx8DdCiD8Gfsd3G24UqMHqP3hLl6bbtt4GOBVT9CvXq5pOJ/0CiTtOMjhwxawYlJac9quQ3BvP1U4FPkUwrl5GT2zvqqkkpkpQk5RyqUc1yoOdF6BY6gXFMuuVSJoqY5pXSH1wNlKfzgtNy2qY58zr5F2Ynp+UqXIVLnXAkZ8jbRPIJFEUOm8oh7NssmWRkjl1tR3GFFuNhdQMTpiESpuqSK2K2isDQQ0W38bPVh/qAWzKB4JITGOe1aOaZIz39u3MbaphRjXPoWCDktRbhRAfBG7COte+Crwb+BVHJNcSS27M4TYhxC7gG8DmNT7HxsP27cy1OlEXWWT+5utZOGVhbRN92xbfL1zByWOb4PvI7MN88DbY/nab42TzkYZ0BPXYxRHt4oW6Q3XrufClK5uW8sM4n3ow6b5z2NQ6gBcCN9Iscu7QoUOHDQNjzIIQ4q+xAXA3CyGuBnT0/NH55ZsXSfuC7EEdeISoQJT2w7gYOwIwiv7Ej5t/7uXmIoQpxSoqEGy2ZkuJGCeWQLp5UENdkJgMitr+O8owUThSrLTKzYWtmUk15gRnFU3sTK09r0HE6qtfj1/H3twFIuWh+xQcefU1M1GdTFWkMztIw3pShUSFXlSlkgYhTFIdLLK+D9WrpzGSKHAI3P9Qi6alV1fSEVNPXjUayWRk7cj+vJao2n1NFILkE4KrIqV01TV+ztTXxqS5C65yFu72a+8NJqR5xeaH2+yd0p07Jqi+CgZqQuxpZD2TWqKKDIkllh42nMrWz1ROUQ2vN9HW0quow5aq2KpsE5b99agftwFW/pr5xw4ahsYs9UaBMeZCIcQQm+z7COBjwBfXgaACXIR9u38V+M/YapkXr8N5NhyGpw0bs4G7b16nVF/fb3rTPJx8HtxzDZx9OSefeTHvftsiLzhpHt6wwLBL8D0u0K6l8f8O16uLt4H2DOqNF9RBSnBY51NXSlIfATzNGPMggBDiN4HrjDG/uF4L69ChQ4fDgBLbC9jDhsEdmlR0GCAM6NySO9UXpHsNovSEMPowPlrmz7sLKmp3pIKz/kYE1Zxg+1cFBIstpSWtnpyGjlOoQ41iZMraiQcVZrMK87RTryszoESomfHQ3+ujHhig9uWNkKTwfIugxrOoMG2DldKQ9iyzt7OmlljVZFQFcuTttj4cySO2/vo0WwDpAoXUyHauLkeU07xCjZKpGUtPVJULivLKrSeqk1EvhBwliQrJvLqqteIkUQ3F01t6Bw/Zx+ARDwKQLU0ol3rB2usJqu8p9RCJRqQq2LhFYl+3TU2OlNAiCyS1jZD+G5FTP7fq1e+Y1KtKkkZvs64kmvr9OFgYNqySijFmERvw9hBslct7hRAV8G5jzB+u4ameBnzIGPMAq6ujOaZx2GYDI4vlN/sX8agzL2ZxEba/fcg3X77Avq/uYXFx2DXNHAeIA5R2fWIXc6fMNZT7xbsX166Opo12kNJz3m+J6ud+G+7/zGGdT10pSX0sIf4B3O+nrvlqOnTo0OEwQQjxfOAKrEPkacaYNalzONwQpUGOWx/CSxnIlb9Pv6orYXxqrv8dmim7s4J2+q6XdbL8/zbixGDAEtktBWJLgUnqNGKo52in0OpB1Xt7qH051b7e1KZ+njFGmlcHpbh5gmrVV021grRagCwryZ1CCc3ZyZicejtwfb9WGWOLr6+/obKKYZqXjflM4Qizf1+bx5xN/GWqG+m9MWbZh1eKuI9VFWmo94lfW9jWJyw7Euu30a5ux1f/QD276+eFdSWDBVhIvargJNnLGDzukat+rUcDjDHfA94JvFMI8UPAq9b4FD+FrRa8EbgW+BtjzPQ/muMUh2020Fks79lyCVu+uZvrfn+RX/mtITt2wM6dQ3bs8DOpXSXq8YS5U+aalvPICrwumBWk9MTXHZH51JX+xb8a+CchxPuxX0xeALx33VbVoUOHDuuPtwA/Y4z5/JFeyMFAKMhcY2L6gEaOhbX5KmFtvk6JFD74CEcwfUgRkR04DlJyXaXCiclCCSjdLatnVD2Mnw39zmbIVKMzNaw11YheZTtQnbXXZCIov8GeXNZrD1UzrgNVjazFNyZiYaYzIkvWwgvkVSBEs8hqVaSB7BSjXiCobcQdqfZ+M9CnN6hnPNvHlqluzH/GwU1+rT7FN1TGpASC6pFl9UyolBqdyKA+JvuZD/UW47xfhGAksOnHQFBRG6+3yFAFjflUXdneW08YvbU67p8dPzgA3JxvqqhwhD1V6Oi6Bmv1qDdFTP3r8/eLsZ3H9TO7Wlubc7mC6qCpazGpWLrnOwe939EK9/fq/2fv7aMsO+sy0ef92HufU91ABGJjgKxE8DqTpcGWlKIEZBthgWaAECiC2ni5rhtv3TuOi49pbZmO96bHaaevIrDmTjmZcTEY5pIpESIwBmE5h8uVEawgc9FJBklohBDFJEAnVXXO3vv9uH+87+/d797nVHdVdXVXV/f7rHVWnY/98Z5dndR5zvP8nudtO3zMN/nwuFcA+GkA/5ox9olLzi134gQwP99hgKMPvQMLX7gNy2/8yM7PBsZhSWSxvOYIrrIKn8+X8ZK/WcBbfmYZx4+XgZgePAisrCSSeinhvFnONwLNp37fUT+nev4qjzab7vvrjLF7ALzIP/Uma+3nz92yEhISEs4trLUvOvNWFyCMI6cAHEGdcJeOOxFB5WRFVwSx+xtgYILVlgsLTHhLSAFHRDPrZ06pgiZSKT1xCWFK2s+hGgbz+BC24WD9vtOBmjGDyhxR1RFRjcg1JhJmLYdtBPQ4h5lknY5OwA3P6Up2KkxkrgIZk7VEvV6cVnmbrA0xGXe30RuoqH2r72BYudoWqUOPZzuP2ZJDwBHS+BycW5d+61N8CZTCCzVNQGnO9HT9rn0SL/Omk9wLdOdIZ8EoDi548LxTWrKu29et5tCejJO1mkKY6FoEokrHjVRT6n1lwcrsK318/U+sasdfEMRJx1vFXrX7nk9Yaxv/Oc8CGAJ4FYBLi6TOz6MjVY5GWFk6iuXFY+dmNjAOS3psBbjmCHDfceD6ZRy8psR7fmMZ33pwBYuLrcW3LBNBvRRx3iznfcTzqURO48fnGFv5P/4cgMette8C8JDvFdwWGGOXMcY+wBj774yx+xljP8IYeypj7BOMsS/5n9/ht2WMsXczxh5gjH2BMfaD2z1vQkJCwl4HM4B4gkM8wbsEdTWHXc27VTL7a7DvmJz5oBupclQNM6O6BnCBNNrbcS0pYoVyQUn7K7D9kdKoWaScWqeeeoLKJsLZfD1B1asF1BODDkHtg8gpFxoyb5ANK4i8cUqg0I5A9hRMIk6TtSGa04QrCeFssjSLyrmz1+aDGoNhBZk17XwlzVwatqmZyY16ROn6EUFl3IQbgXGDLFeOBHuinA9qyJwqb/w655wN2fSunSOYGxNVNc7RrBdhO6N4SFBu1guvtrYEdbI2PK212viqnNp/GTAZF1hfnUMdzbTSNSNllap3KCyKLM5Wc9SnWfvpYC3b1u1SAWPs5T7l/AEArwXw7wB8164uajdQlsDyMk4cvxGj294ILCzg8JGPorzpLRidHOHEp0+4za4ud2YWMA5LUquBoOJAGWZRBwcPY2nJVbYmXLroW85HJ8/TP4j+fCr9m31s5bycfrMVNL8Gl/D7vXAR6BmA9wF44TbP+y4AH7PWvtbHns8B+FUAf2Kt/Q3G2K8A+BUAvwxnP/kef/thAEv+Z0JCQsKlBwsfMMQ65C7MisZqZuYsvACACQcnZbTpfQCnfeK+0rEMMUzxjCg0b624q0XH0sm4ceTU24v752KNgGwMABZUWqaZI9dRzYw13NXN9GdjKdRJ885rcliHMCQAU0Qsrqeh/cheWgyrTrIsKXhETgFHHCmkiI5BBIxz61XULHSgho5Wr6K2gUHtmutx0c6hRuuZNT8KtGROZk2YM6X3HIiyD3CKr4XrJqXwIjfDq7VX3IWFrh3RdwFUAkzboLiSrZf2NZpDVZlL5PXrptAjIt+kKMeBVFbzoFI3dYYsb6aUVNNJNI6V65bMVpPpueQzwiYldRP4H+FmUX/BWlvt8lp2F2WJ+dHNWJjcieXFQyjLczwDGIUlfWX/UVzlCerCAnDkCKCUE3bTLOqli12to+nPp5JFPX7+HFbSbHYm9SYABwH8BQBYax9mjD1pOydkjD0ZwIvh/qcIa20NoGaMvQrAS/xm7wXwSTiS+ioAv2ettQA+41XY79prnYYJCQkJOwLLuvbehrc236jzNJDTGH1yCsAOdJgXDbOqQOhKtRMZiCl8xYxtBEwlYWoJq4RLgOUGvFCO8PokXwAtodbMJQ5PTFgfEVRbOZJNPahmMk1QY2tvn6Bmc1WHQDo1NXcE0m9HVTOkWMZkMK6gYcIgy5qO5TYmhsYwGC48Sc3aWco4kVg5Agt/zD6hI9trnB4MuEoZImj9GdiwFp/WK3MFTgTPE0jAVb5kwxpcaN9JKoK12mrWXkPNgz1XRx8FiNS62VPZSeedrA47lTvhvURfKNC1NMaFHjU+QTl+L65ztvveYjuvCDbf9phNnUHPSA8+E/Zyui9j7BkAfh7AT8GJBBLAtwB8GsB/sNb+0U6cx1p7y04c56LAaIRy6R4sLx7CQv0+LL4HWHr0nnNHCHphSZ//WImVL1BYUktMl5fTLOqlil2to+mj3+fbr6vZYWyWpNbWWssYswDAGDubkufvBvAIgPcwxp4H4HMAfgnAASKe1tq/ZYx9p9/+mej2sT7kn0skNSEh4dKDRbD3kvoIIFS90Dwq02yKqLKJ/5AflFM3l9rZKuootWt5OL41zKmcjevjJCuuqykx0/OoM1Rbq50Siyqam51ImNWiQ3pp3hRoOzqNFmEmkp7vh//Q65b6OIO6x6GaLFhKhTQh5CnYbCNlj9Jk6XEgwD2r7qyamWD59U5n2rep2oAmUg1VLdHUGbgwKIYVMmwcqEopwRsprWG76JqIXAE1OgnIpJoCmLIDh07TOqr28dUxqpYYPzF0natoSbm7phwK3fdWjQtUkwJVlaEomnCdBsOJI9i97tNYbQ3rFwZ1nfnfJUeWN9g69qZ1lzH2WriO1GUAP2ut/bJ/fh+A5wN4FWPsrdbaG87iHH9qrb2eMfYEECqQw09r7ZPP9n3sKZCEuez6SBffAxz76p04euWhnSEDcVAS4D7gf+om4MrX46ofvh2f/1iJZ391AZebZRyOwpKANIt6KWOWtTyuozmviC3q37PogpTO4XzqZknqMmPs3wC4jDH2PwP4nwD827M45w8C+EVr7WcZY++Cs/ZuhFl/XaYkAsbYrQBuBYArr7xym0tLSEhI2ANQvEtQ0SbpIjOOaEakkVRMSvvtWIKrNpBolmoKALZxCiQRVBuTRa+ihtnJhnursK+s8YnCFm0Pq9U8qMBmLe8EJBHRJHIaE1RdZ0E9BBA6Ppv1ou0LrTNUqwOftJsFchhbbakHFWhJJVl8s1x1rL1hn6geBkBHOQTamVLXdWpCb2n8OlWsUM3KZDyAMRyFaB2Ws+ZaXZVME/pYjRIwwnSuRdhW6PA8EwZyWEON86lt+o9jNZbIaZyS3FRZIKDcuPXHdmVaOxHvpnHKp/R1OdxfX5krZF6ZtobD1DzMoMbvXfS+gMiypkPAt4K9SFLhRqI+0H/SWrsG4FMAPsUY2382J7DWXu9/bssZd9FhZSVIl6OTIyw9eg+OXnkIS1/9IMqTb9rZoKQDJfA3dwGwwFVOyD74cheWdP+nu2FJCQlAty+VcE77UjdCZFE/15U0m033/U3G2EsBPA5nObnNWvuJbZ7zIQAPWWs/6x9/AI6kfoNsvIyx7wLw99H2z472fxaAh2es8Q4AdwDAddddN8PnlpCQkHDxwCoOaxgYt0FFDdZdb6WN50vtWktUbCVbazC8JdLPmRIh7ZzLk9NwXtUSO1JRndVXO5LryW4IThK2rbKBl2n8HK1eLdCccjUm/ZAkIqoAQmgPck/EpAmptVwa6Er6zs4iJPuSerpRdQmRYZo/pdnTjdRKY/gUOQXQqUtx2zmyG6PxKqqQBtrbgU8XOmQNhwaQFU0gqP0gqM7aIvsuAIhCdcioGudgkbLO0b7GhA2PY4JKJJ/syeFcigfSHV8HsveO/YyvFAbC1+0QQSWlWggNCO3Tfp312XiiKoa687sHnMqbb0tJBewe/ERgrV2d9bwfmfppuGyQ9wOYud1W4cMqn43oc6G19i924th7BofdB/2pGcCTb9qZGcC+CvW1D+Kuh+/GgftKlAcQwpJe8YoSH3xHUk8Tujjvfakb4TxW0pyRpDLGBFyx808A2C4xDbDW/h1j7GuMse+11n4RwA0A7vO3nwPwG/7nH/pdPgzgHzPG7oILTDqV5lETEhIuWVjWC0rSgXAyzZxi2UvkDbbdzBERNwPqlc1IMTVV5pJ6ac7UH9dG85ZWiY6KygsFMYxSfIXp2IkxMJH1l3eUVOpWJRUUiFJ7/eFU5RTWhtTAWoIPtd/eKau6kmg8Oa0neYdYbTTbqbUIBJU6TeMQH5q97IcekcJK5CwmqPF8KZE6srVSeBPnBsZwTMaD8FgIjSxrQtWMW0xkM1YCPFfuZ7wmLbwK2v5+mLCBoIbKHmk6BDW2/8aP42AkVWch3El4q3F87br7u+TdmKAWRdO+t7xL/LXiYNx9GbKZRGTA/W769u7NgOcSc1c9fcv7AQD+dHu7nWP83wA+BmAdwIfQVgNuG4yxY3A5IV8Gwhi3BfDjZ3vsvYhzOgPYU6EOPLUMIUnHj7c/b789hSUldLHrfanAea+kOSNJtdZqxtg6Y+wp1tpTO3TeXwTwH3yy75cBvAnuM8kyY+znAXwVwOv8tn8E4CfhotHX/bYJCTuOB++/f9Pb5pddhu9/wQvO4WoSEjaAZrCrBWwjwDIiNP5z5birkAKAXc1h1nKnuEYwVQbW9MhGwwMJtZqDo6tc0byoIz0GPPcENdOAPxYbeDJCxJnCmMYS5tQgbGcqGdXWNAAy9+lYtwTM1aGwYEV1M5Oup5OSaa12hEp5Wy8RK0JMQmNLLiEf1CiG3UBTY7irsOEW8KSwT1qF0B2C1Yb+OFV2lnpb19lUym1QGL26GNuM6ZyqznwHqw5pysZbcWlN1NlKdl8mTDiW1Roib6DGefgSoA8iqPUkDyFTRFCB6e5WYLa9F3AEtRi011SIlnxT52k/9ZdeJzW4r6LGx9gKTKWwdvLRLe93ocB/QX/UWvsl/9R3wiXxjuHyPHYCCwCe44MsL1mQnTK2TsZ2yh0hAz0Vqry+xPJyiRtvBG6+uRuWdPBgCktK6GLX+lIJp6uk2Q2S6jEB8JeMsU8AWKMnrbX/ZDsntdb+V7hKmz6mAgB8qu//tp3zJCRsBWZtDc+//PJNbfu5Rx45x6tJSJgNqzn0OHdzoIVypDRrP9iz3rbm8QFMlYEXzTQp9eFBdN/4Xso2qTbzBNJBrXs1U5qgorJh40hyTGJkT031IUzqsTZzjwgj4wYsp30zWK0DQSX1lMJ7gCjcx/MVsvYCZDft/lnLcoWMqlo8+RISgHLEy9lpW5XPGBZmVrnUgBIw/n5MWsM166mBseKovD0ZQIecNk0WbLCDYeVmNKM5WDoG2Xs5N46keoU3rBUA9yq49ASVAqX6M7Vhnxm9s3R960mO2iuhMZGP/40QtFfEaba2biQ4N4GgxtZcJlolNk5S7qNP/GltcWXNVmEBmL05k0p4O4B/zhj7WwC3w7Ue/AEAAeCf7dA5/grAZWjHrC5JnBM7ZRyWRCrUNUcAq4L1t7x+GW99a4ljx4CjR1NYUsLG6Pellled5wClWTUzu2n39fhP/paQkJCQsJtg1hFOIqhA19o7kYBXM+23hm0iLxdtoi4Qkm1josqkASoEpZTFVSNKBPIj9lXghQLfX3XtvTEBilOCJxL620OY2im9sX2Y/gpRL6rRwtt389DXSTBKBNUQgE+cnQuv15Mcqs6CjXfDS8gNhGzJVzi+Yf4c3NXMcAtj2GlnQfswPSIZZmqjYKIsa1wAkTAY7h8jH1aQeRNCivrHANrKnJa42vCY6miYsG3YVG9dtA6ZN1A+MZdA86cbdbd2jkPBUcZZfKtJgfG4QJY3KIoGWdb49xup2QCMaFN84+vBZ/yuiKiS6sq1gdAG2IaSCuzZ4CQAgLX2QQBvYIy9CE5B/U8Aftxau72LMRvHAXyeMfZXADirl8gAACAASURBVIIEbq195Q6e48LEiRPA/DxQlq2d8v03YdFeh6X8/zt7O2UclvTYCnDNEazfexxfvHwZB69xKtSDf76Cd7+7xA03AEtLiZwmzMau9qXuEk5LUhljV1prv2qtfe/5WlBCQkJCwsZgDC1BFV1ySKm8oHCfcQ7bcLDMtNZgIIQt2SoiqPTTk1Pma1hi9YxrDl40jqAWTUfFJdiJbBOG/drsOIP2qigpp1Q3Q/biMJOqOJpxEWYj+zBKwEzcsRpvT43nQwFHijYiNKTW0fxn6DRFO3eqelZdIoaz1kNJvRT000+o1T3bKimMdZ1hbv86BvvGyOeqYGWerA7dtfBqL52fE2HmFrJok2650JDDOoRHAW6Ol5KP6bGuJeSwhtU82IfpelLI1JnAvKILdEOShNRBQY1tvR3U7fH7yb3xthSe1CBrk5TJFlyfeY2zsJdJKmPscgBvgBt3eg2AmwH8MWPsXdbaj+zQad4L4F8C+Etg6vuNixvz853hz/IrwOKnaxz7oT/B0R/dATvljMqOL16+jJcdchUzQImbDpWwFnj7290uaRY1YRYuqL5UYLpSCcBT5rCjSeFnUlLvhquLAWPsD6y1N+/kyRMSEhIStghmuwTVw44zWMNgGwF4smKbyFLLpyNOYysvAHDTVVpZZoDG24KlgZBVCEpiww2SVn0XKqmltpJOzY3V03jzqBMVcCm+LoxoNkGNCVVTS1Tjok3VDcqc2TAwaRZi+2kgvE0WukmJINIa+qm8fZtqXGsDT7r6ttViWCEf1F4FdRUzRgvIvEGtikBU6fxc6hAmRbOngOtClUV7XLqeWvOgXlIFDZfelr3avg+jOKpxMTVzSu9BzOAs9cQR1MaTxjxztTIuxbfdnl43mkP30nlpu1hRJYJqDEflVXQpTFCet4e92ZMa4W4A74HL7XintfZWxtjvA/injLGft9a+egfO8ai19t07cJy9h7J0jHBhAVhcxOjD78LSQo6jP/K2nbNT9sKSDl5bhlM+73kuffruu1tSurycZlETpnFB9aUC05VK3xjh6svx3Tt5ijOR1Pj/7Dt64oSEhISE7cGq3uyp4iGIyFSyM0PoVE8Vkn0BAI3YkLQGG3AzTSrFsG7nUMOxWsW0Q5w9aTZV5tbmE4M770O7XlOj2oRfTd2cNIMqp62gZEmt/PwkwQURmU6tDOPtPKQBn0kmO2vy9uc46bdPUGm2NVw3mrfEdI0O0M6Bai0gvTxKtlhjWLA102wokWOy/gIIJC4GExZGi6Cckl2a0nop0ZeCp0Tlzk0hU1q7hN2mydA0bk3G8GCZnnVtmibDxH8xkOUNhNRBTebadEhqbOVt6gx1nQWiqjVHU2cday9d76pqVdTacAiZIcP262f2OEl9MoD/CDde+xMAYK2dADjGGHvGDp3jc4yx43BtCrHd99KooClLR1DvPIaFn5vD8hs+urN2yhmVHWVZYnERU3OotJxEUBMueMxwCZx8BF/eyVOciaTaDe4nJCQkJOwigrUX8GRQBgttbNflRRNIZVAzibA2okNeGZwV2FQSyNAhqmJYg++v3FxrZOcNnati2vpLBNVMskBQ275VP/vpA3iaOvNJvU5F7c9lcm47mp5TSn3yrGityiEl1veeusRcSlma/pPX74QlghoTRaqP6ZPTOFxoI6Lat7JSTQ3NmKo6C8FIBOkrW2hW9ExwHbKYSkLm0J7IuuResgjHBJXeg9UcDbIpkt3UElmuQt/s6qn9ABCSid02MdnsJivTlwaNn4OtI3WVangAQEgNrQSU3z/PFJTmHVWV622GJ81Q5fcQboOrnKkA/Gr8grX273boHAf9zziy/tKpoBmNgKUlrPyvN2D57hWUPwbg6m3aKfsWyG+MgE/dBFz5euDa20Nlx+f3LWNpqcTRo2kONWEPo+cSOLV+7ImdPPyZSOrzGGOPw312Gfr78I+ttfbJO7mYhISEhIsBvl/6XgBft9beyBi7Gi705KkA/gLAIWttzRgrAPwegOcDeAzA6621X9nMOWwlnbUXCASVwocEXAgSL1SrmArj/scd2249QWW9ZF7ujwnPjzhXgaCygXKWUeEJF1QnMIkUOEvrmmSdc1jFQ5WNrh0pMlpA1zIQNc5tqF+hxwQhNBRkp5OUnnfbditT+im31nBIr2KSbTgOUIoJKh0vJnWzkm4JpCgCLVkLiqwnjlpzDAdVJ3GYFF6qwuHcOpIZhSMRwSQ1Ne49BYBmnE8prVzoTidqvV74DlQ5NYcLINiEp2ZGI4I6HhcYDqtQnWM1B/IGqN3aYos1EVS3FhPsvwAwGQ9Cp6qQGly3gUx07aUwUGjVVbndhN89rKRaaz8E14d6Ls9x6dKj0Qgnjt+I+TuO4fBNbwFeMgIWFjC64whWvlNtvXqmb4H8m7sAWOCqW3xGU4nL9i3jw+9ZwfKyO+43vpHmUBP2KHougfM6k2qt3e4QSEJCQsKljF8CcD+cVQ9woSS/ba29izH2OwB+HsCS//kta+1zGWO3+O1ef9ojW9bOeXo1kwiqrmQgBU5FbWdMw5xoXEOT9ey3pKp55cnNpepu1Q39zAxYwx1Zncg2vKlxM6lEUCnMp3MeSqBVLUElEkeQaIOK4vlUmlmMQdbe+Hnq+GyqLJAeIqVEXKW3kJJVlo4fFoBWPVW1hJDdIKk4BZfuz7L7OkXRz5AS2YzOR8esxkUgqkY7m7H09lijeecaxeTTJfP6uVM/v2p0pAD7Y5FdWTWtnVZrEchpJ/QJoeUHWotgrS6KVkGdtV88C6y1+8KkXUNrXx6PizYYqW6JqTFudpf+5YZtDEc944uBM4EXEvu/+2lb3g8A8Lnt7bYTYIzdDPcr+Ki1Vs14/UoAv2it/ac7cK4DAP4FgCusta9gjF0D4Eestb97tse+4LGygvnFY1h44DiWTx5EWZYY3XEEC1+4Dctv3EYuVd8C+bUPAi++GzhQhoym17ymxC3/i2OjRE5vuSXNoSZsDtTnG395Evf5njdQpRJ9IXOgxNWX//h5nUlNSEhISNgCGGPPAvBTAH4dwFsYYwzONvfTfpP3Avjf4Ujqq/x9APgAgH/FGGO+H3omunOe/gO8V1DDGuIOU7hQpf4xALje1Ew7qy6RzEihDQTVk13mLb12YICGgcGTVWlgV/NAhG0l0XxrX9urCgSySsop2XydRbVLULkwMMLMTPi1US+p1Rwyb5DlKiTPOpLj1uusrO11aWqJfFC3KmkendPf58YR23AOb4kV3j4dPwe0NSm0HrpORM76s5399Fs6Vmv/9WqyJ5Vxh2tfTaWfqsraGV4fxNR53duGiXA3pAz3SGar9lJoUrffNVZJ41lS0ye4ESGn4zdNBu3XSDbeGCFZWQugdnbiGJx3ldjNwlQKTzz4zS3vdwHgEwDeCuDXGWMPAvhruM7674Sz534TwG/s0Ln+PVw4k8+XxV/DzcFe/CT18GGUAJZPHsTCBxaweN0ilh5YwvIbP7L9OdSeBZKsv3FG04EDzuYbq6eJoCZsBuekz3c7eGylJagAcKA87zOpCQkJCQlbwzsBHAaC7eVpAL4dqSEPAXimv/9MAF8DAGutYoyd8ts/Gh+QMXYrgFsB4NlPLTo2WlIkyf4q4v5UIIQZ9efyrCeonWdjcsqtI7mDrohjyWaaWVgATLOgrNpKwqwW0OMcupIwWoQU2j45NYEYiU5abfycLJoOUTUROQw2Uh+OJISGlRymbhONhXRElZTKLFfIiqatlOmFM9Fjd708CVXR8Wak+La9n9RDOiV6BcQkj8hpf/ZWawFuDKCmQ6Ni8hluSoQZXhdA1d2HCGpTZeGctO6615dKxyaiGpNXIXTniwRaa0xQg7KsTYeME7kUUqOqsplkU/lj1LVEDYk5tER1Vl/rpmH3ZrqvtfZxAL/GGPs/AFwH4PsADOH03X9mrf37HTzd0621y4yxI/7cijG2kz2sFzzKq0ssXreIY586hqMvPsvqmRlBSTFR3SgwKSFhMwh9vvSlyr1Lu9OVes20antqHed1JjUhISEhYZNgjN0I4O+ttZ9jjL2Enp6xqd3Ea+0T1t4B4A4A+MFnP9maSdbpFgUQCCofRAm94wzItJsvnXUiSvmNSC11qDJpgIFqZ0wp9Eezlpw2HPCkExMJW0lHUNcKF+ZTyVCDEs4ZEVTAVah0X+9eEi41TBQqJKSBMT2bsuHQcISScxNmTiE0GO9W0XBuOmTUmJbYAuhc05hE0jni52dZe/uzsjE66qNpSdwsEmYMdz2p/v2HGpoo5TeQbD/Da8BRrxdhlpWUZeOJOq3bndeR3KbJOlbbTliVP1df5Qa6icWxKmoMRzUpXG0MBSb17dlSA8ptq3xas6Y031oizxWUFlBjgTxroL0uXW+iy7UPiz0/k2oA/Lm/nSusMcaeBv//HsbYCwCcOofnu+AwOjnC0r1LOPrio2dXPTPDArn+8QV88fJlHHx5SRlNOHQIeMc7UmBSwvawo1+qXMBIJPUixl9+5jOov/3tTW178v778fzLLz/HK0pIuOjxQgCvZIz9JIAB3EzqOwFcxhiTXk19FoCH/fYPAXg2gIcYYxLAU+BsfBvDsimCCri5SiKoBD3OwarTq1DWMIBmVYmcCuMCkoYqpPgyeDvr2Kf5qnYWlSy+6vEh9FrRWVtsgQUQCCoPgULdx1q3f5aIEMlcuWqWKMwoBtl6iWTFwUcSakpdJHJnDA8KI9AGKAmhAaGhVdGm9ioOQXOq3nI8K2Qofs/xTCY9dsc3IcUWAIzgU9U3dB6a1zRwFmA+ozqIrhHgiGk9yf379BbaJgsEtbX5Cl/nIwCJYMWlTwVC6Kh3drbNl9YIoEN0AaBpMsStMXEYkquV0eHLgfEkBxcWxn9BIaLfb+3XDiAQ2a1iL5PU84S3wNXPPIcx9mkAlwN47e4u6fwhtktuq3omTvQlCyQ9f81hfPFyF5Q0uq/E8ePAkSPA8ePA7benwKSE7WHHvlTZKdB/AzuMRFIvYtTf/vamieeX7r33HK/m0sVWvizIL7sM3/+CF5x5w4QLEtbaIwCOAIBXUt9mrf0Zxtjvw33ouwvAzwH4Q7/Lh/3jP/Ov/+fTzaO6k7Aw3xkrdp37mWnV1BkVKyyuneG2S1CBNiCJ7jc90qk50DhiSjOsQUGNVFsmDLS3dtIRuNAdJZWeOx24aNVPsvEC3QoYwBGhYlh11UA5fWwuNYwSaHxfaNNkIXxJ5ioEKQlpps4R22Vn9ayG7Wa8HhO8uJ4lO41FuKmykDhMhJVLvSFZpX3i33ufoJLNlwglVbwAbl5UCgPkbc0MEXHtbb+0fqqQETOuMdXJFMXp+03XxwWqRqLwUUm8l1pc1xJKi0Bgt4NEUmeDMTYP4GvW2r9gjP0YgF8AcDOAj8N9gXZJYOXhlQ4h3XL1TJzoe83hrpoK4ODLS3y7KHHjjcDNNzuCSsT04MEUmJSwNZz1lyrnAv6/gfOa7puQkHD22MqXBZ975JFzvJqEXcIvA7iLMfbPAXwebSDJ7wK4kzH2AJyCesuZDmQMc52X0nRUvNh6yosGjNswh2qqtmqFwpAAdHpSO3OsPrU3HLuSzs6reLdKxhPU0IXqSZfVHFxoX4HCZ5LS+DmaXe1v57b1llOpZ/acAk5F7FfHEGb1jFKAUD3JO2m0xpN6InlN73ymjgkqnznPSZSMbLBZ1nTsskT6ZN6ELtdwnSNL8azn4jod01NVaS6VEJNr6nolJZdsvkRON6p2iYk00A1S0lp01OD40wQR1Pi4bp5YoG4kNKUMa4G6FhDcICclWAunmCqnnDqSyretosIC9ixGWi9y/BsAP+Hv/yhccNIvAvgBuPGCi1NNPXECJ57xIOZfdAvKq0uXiDoaYXTXL2Dlxc8JtTOb/sDfT/T90lI3UAaOhL71rdOzqMnum7BVnPWXKucC/r+BlO6bkJCQsAdgrf0kgE/6+18G8EMztpkAeN3Wjtyf2WzrU6zi4PtUqIyJtzRV1qb17q8dEZ0RsATAWXgn0qmoAOxq7ogqADRuZpUeW8Nhagnm1TQBdNRUUSig8ivvnKNLSOMgpY3ApatyUWSN9afJisaTPhtUUkrJbap+sjHvKItCaGS5Cs81tZyus4kCkvqIQ4DCvKdqCZxWAoPhxK0zbwJBzQe1uz6eIFM9TkxK2+Tf2SwrTj42SkxZwAFnuyX1FJgmqAQh2xofrbpKb1hjn5RTIm/vnP3tCHUj0dTtuYl45rnG3LAKyb5KixCkVDUSkw1mqjeLpKRuCGGtpfGC1wO4w1r7BwD+gDH2X3dxXecW8/OYf/OvY+Gh/4jlN3wI5VeA0ZtfjYUFhuUrtllJ20v0PfFeVzlDBHQ0At79buCGG9xMaiKnCdvFrJqZLX2pcq5woMRjT2BHlZZEUhMSEhL2Ejxf4V45ZMKAwxFAFtWkAOgQT3q+Y+kFQvXMFKJKGv34EKaSrtaGGzAz/aG/VVgpMddbROvudvS81b05US2mQpM2gsyaQM65NKFLNLb2cqlRrxczFVFSFYUw4Jmb45W5s0dPxkVbweKtrXFVS55P21eJ2FG1io6CmZA30NoRVSJ9PJ59pevH2w7WfrIwEddYSe2cX013x9K6VO2IYWw9rqpsKiQpXDdukA0bDIbVVAAUi6qKaNtwTTf6dxSdczweQCuOKgpAynONJ+9fBwBIoaE5D/ZepTkmlUTVbFNFBWDBYBJJ3QgimpW/AT5B3OPi/XxYlih/+24sv/nVWKhvxOKKxdJC7gjrdj/o9xJ9X3ptiZctlFj246k33QRYC7zdl/ykWdSEiw7fGOFpT8KOhttcvP8TSkhISLgYwRHIqSOm2vWi5grc23htJZ3i6WEb0ZlDnYmIZFD/Ksauj5UIKi8a2EZ0e1S5Ac/V9OwrVcQIDR39qeknEgcl+DQKaqha8eRP5ioEBfXVRHqObL4ya6CVUw/DW9Wkoro1ZkXjLMAAsqzpzKhO94F2+1FnEdQ4TGg4rDAYTjrVNLQWBRnsvv3ZV4KMrq1R3P3VjqTLEFRURUqsf79EUDvvXbWVMJDTIVRC6EBQ48qdWE0mm/NwWIXzj8dFOD7glNnacKBxyvTa2twUQQWAuWE1830DTmnVhkGK049pnw6iEHjSc566vZ3v2/Zp9wreD+D/YYw9CmAM4P8FAMbYc3Gxp/uWJcpX/hIWP3UMx34MOPojb9saQY3DkmgG9ZojgFXA9cs4+KcL+Pidy3jZQonnPc8R1Lvvbknp8nKaRU24iOD/G0g9qQkJFwAevP/+TW+bkpMTdha2VVGlDuqpI5GeuFWy02NzJoJKpJRJ4+43AtawEIjECwUxrF24ErcwleyGL2UGaFoFkBQ/FimbRoug+ALOBkyBSTS7Gif7tvu1XaCAI235XNUhfUbzbp+qt77SjKrWIhC12LartQtH4tyA58b1k6KtYqG6m6qWncoVSr6dRVCpJkUKjf1PWse+/euQeRPCkZpadtbTzMgVikOcKGlYNVmwARt0Ca3WItTNAE4prutsqv6FZkLpOTE1AQsMhhXyQUscOTdhrXEYFHWYmsjWPB4X4dhxrY+bPZVhBlUKAy4s5oYV8qy9ALSvFBq1lkGdFdxCSgO1AZE/HXSl8fgD39ryfpcCrLW/zhj7EwDfBeDjUWgbh5tNvXgxGmH04Xdh6dVzOPrnFkvZu7eWkBqHJT224gjqfcfbWdTrl3HwsRUsLpYzO1GT3TdhJ3Di0ycwf8V859/t6OQIKw+vzLQFnzP4VOtT6z+eelITEnYbZm0tJScnXBBgfctmpgGvfgKA2F+FoCS70WyfV0atv28Ng6mcisq4txD79F8LzLb7ZgbMe5HNpFXLqL+VKV9BUjSduVMKWhKFcs9rhOoT0wsnolRbOSMNl/pU43nUvkpItl2ZK0iojt32dBDCxG0qrbq4gYIqhca+fWPM7Vvf8JjKk9VZ9uF4VaQ00++5nxgcK6f0/kg9jVVNwFly6bm4EobmUQfDSYegtu9fd5Tn/nWj31FcFxMrxFWTBfsuocga5FkDIXXHHi2kga55h6AS5AZ25zMhzaRuDGvtZ2Y899e7sZbzhtGonUF9w0dR/hhQvvnVWMBNm7f8bhCW1M6ilhjdV6ZO1IRzivkr5jvJvnHy73nFNeeGECeSmpCQkLCnQGohB5DBKgEmdaibYZXxSqcBLxrwfTUwaEmqHWchUIlUU8D3pXoSSwQ1zKDGqqk0QGMDCaZ+z/A6dwqvVSLMp4q8AfdpwaJQEFBo1ougrhJpFUTYvHJJ5IdLDfhj5XPTJIrU1hhaC+ixUz2pN5SQZQ2EbGt6Km9VJcIXiKBqSSEXphOSROm2swhqMagwt78lqCqaCY3nW2eRZOpW1X49lETcv87xeomg0rpmBSOF9543U92m9Hwxw3obW4WZMCGxl95HeI/aXzuf2iuNe2+UzhuDCxuqaWKCqrSAVi7Rl0KVSEHdjorqFppIakKLE58+gfnPPIiVt9yCZZ/uO8LIPf46tpaQ2gtLwgFHUBcW2i7U1ImacC5Byb4LH1jA4nWLWLp3aXeraHYYiaQmJCQk7DEEcgcAioNrDqacVZd7lTEmqKSk8n01zFoOjGcTGABTCirzFt9ORU2mwbwdeIrEGg5mjCOpmrdhPxTyNPA2UcXbDtXI9gsAVrOWoEbhPaSg0nbGz4luBGs4Kq9YAtTz6Wy0lAhslMBkbRhsuASaOSXyJ4RGg6y1+5rpkCRnEdYYDisUwyookKqWYeY3Pgcl/cYEltYpBXW09lJ4KcxpJkF1KqruEXaqg4mJIT0npEaWOYIa97VS2nF83rgyRyuOelJMXXMipTVmzxhLYVBkzZTKbfz7qZoskNq4wkYpN5+6VVggBSclBMxfMY8F+X9i+UXTylN5dYktfbTvhSXhQImydGFJqRM14XyhvLrE4nWLOPapYzj64qMXDUEFEklNSEhI2FuIhk2JnDR1BpE3jhbkCmL/xmE0thFT86rMWyqJoG56KTPUvXBc6eZMWaS8zdxuRtcrE7ZDToEuWaVeVXe/nVntp9z2w4hm2YRpu7hP9Uxr1lqEACIipgKOxBVFE+Y1+/vQeuP307fvEgm2msMIDoE2sTlWXuNwJELj51BJ4c2z2e8XcATQVeJoZHmDLNt4zZuFU1E5qqb7b8gRUwUurFOaiyZYkIlQUz+q0WzDTlS1yfTnPuz2c5cSLjLsmPJEYUk0g3qgDI/LskydqAnnDaOTIyzdu4SjLz6KpXuXtjZbfYEjkdSEhISEPYZ+HyYFKfFB4yy6NIM6zgDFXScqAP3tYbACA4BtuIvgybRP7fUk0ZMiazhsxUPtDJFZClUCXEgTi0hFTHJjAkr3aV6VVFRBa9UcMS0VuQJotpR6OqOuT6NaFY/SfI3iLmCoNxdpjO4QLms46nGBpsrcfa8Ikqo5i5xSCBGRqtoTsSxvOnOdRFCtdmnBNHsaE0yyMk+8zbhPyGn+k/MuQQ21Pv69UHpvTHiJoBJ5Blpbb2xXzvIm1OLIvAnXCmhVVPqSgdRbZyVG6F2Nr0k1KUK3aQwpDAQ3gaAK7oOqoi5ammVVWnSswfH97aioDizZfRM62JbyFKf5Am1Y0mMrfg61ROlDlEb3lakTNeG8YMoJcFXZebzXsf3ysYSEhISEXYHVblaRyCoTfv7UJ/wyf7OGwawWMI8P3K2Sbs7RByuZ2v/0ZM0a3h4jM7ANh2142Ib2s43oVM6E422gwlJNDqmrupIwirtZ1YG/Fa77lEvTsf9yocGEDeqq0a77k26koDZVhmpcYDIuwhwlWZY5d0Qry5xi2NQSTS1RT3KMV4c+TMkl8MaKIhHruhdERAFEsXKaZe5n7mc7tV9n27lq/E2DizZxuGkyVJPCEb4Z5FgI3SGogXA2WQhIItJLFmTO3ewo5yasK8tb9VJIp566Kp9p6y3gCC8R1P6/vbXVOTx+aj/W1oZYfWIO43ERZmC14VMzpGTxzXMVVFRaH9CGK9V1PJ/aHmfb86i0Zsu2dTsTGGNvZoz9N8bYXzHG3s8YG5zVQvcYGGMvZ4x9kTH2AGPsV06z3WsZY5Yxdt35XN9G6CtPo5OjM+9Eab7fGLWP7zsOPG0+zKGO7isx+sbhTifq8rJ/bROnSEjYKlYeXukQUnIKrDy8sssr2xkkJTUhISFhL4G1/aKOwJl27hNO5WSaA6SujXOwKlJG/Qd+mhkFAI6NbaG0DzM0ozod3mMVDwFO8ewq9bcCgKm7f274aZJazRlspqrO0FSZ6zetOZoq61SwzAokipU7AL15U0cas951iNNyY4IaziNnpwPPUmLjftV+bUuWN468eUI6C1SpY/yXCRT2FHe5Ui8rkfIpyzQ3gHTBUbm3+sZzpuFc/v2RGizCrLAjqE2dBeWUVGuy6gIu7IjmSUlF3Qh1k00R1BgUnBSn/G4F1gLmHNh9GWPPBPBPAFxjrR0zxpYB3ALg3+/82S48MMYEgP8LwEsBPARghTH2YWvtfb3tngR3nT57/lfpceIEfORuqzw99wjKFYXydcubU542SPPFgRLlgZaMpk7UhPOJWTUz5dXJ7puQkJCQsCuwkEUTbLJMmNBH6lRODkOq1ppPtfX/p7enUaRIGbUNBzL3MyaggYSaHkn1r7v7XaLBo/lY800ZXncEtXGK8KTtL43VYReeJEIdDYAwf+kCjxw5pbULaSBgkEEFiyxtH2y/NQ/kVGuO4b5xIGRaixCeVEe2WONJamw9JbLXea+R6urIpguK0mP3HmLySaps/5ikbpLN1hhnX253dKrjeG3YOWcMmjMFECpj6kilJWU19NVGKceBcEYE3mo3Z0qJxJ1kYAqkMi6Rl0AWXylMWyUjDaRo51Brv208N9yfRaXKmVA9sw1FVQwEnvLcy7a8HwBsopZeAhgyxhoAcwAe3t6J9iR+CMAD1tovAwBj7C4ArwJwX2+7v4XFuAAAIABJREFUYwBOAHjb+V1eBJI6l5exkq84gnqrSzSKlaczfrCfkeZLKEtgcXF6DpVeSwQ14VzigulL3WEkkpqQkJCwh8C4hSgUeNGARQQD8IQRHKgRiCoAMNUSWbddd+4ytu5aw4Gm+5z2Nl5KEY5B5DLMlkaKKi+USxgGIKoMzTf3tXOqRIgqGdZiPDmy2s+Y+lAkLltVkAsDzi24NCHl1l0X07HFGsVhJZ8KT4r7SYnYxuSMiC2RuqbJpipUNgLNbgIIVlkhNIxua22auq2HaWtrTCC2QmggOg6tzRge1hfP19J1EUJPBUMRWdXeDkwhScF23KsOAnhPbeadc8V2ZKqy0YpjfdxW/Ah/zDx355ZoySmAyOLrCG8clkSJw3RN+pj13JmgJxrf/tKpLe93Jlhrv84Y+00AXwUwBvBxa+3Hd/xEFy6eCeBr0eOHAPxwvAFj7CCAZ1trP8oY2zWSeiJfwfwdR1AuLODw4iKwtITRHUewkq/gMMrNK0+9NN+7RiUOXFuiLJ2dN3WiJuwWLpi+1B1GmklNSEhI2ENgIPXUbJisq9YK10PqlUntZ0oJHYKqpm2WrYLaqqREjONjEEGl+511evIcjtPwzn4EXWcwWkBVGXQloWsZFFTqPlW1hKqi0CTDHDEWGowbyKzpKJVhVnfG9ZG52qCftCWARCqpb7SJQoLoFvaLrl+8Lz0GEMKUaG6U6mCGwwr7n7SOuf3rGEQdpfH+xlCKr5u57VbYbGwPpv3jNRBBjY+tFQ/zyMa4lOO4y5WU5GpSdDpT6RpU/jnpO1SlMIGgEihQSflzGsNRN9nMoKXw3rgJt7PFWcykPp0xdm90u5WOyRj7Djjl8GoAVwDYxxj72bNe7N7BrKHdYKxmjHEAvw3grWc8EGO30jV+5JFHdnCJDvNXzGPhgeMYLb4COHYMo8VXYOGB45i/Yn7zB4nTfK+9Hbh+Ga85sIB3/eoI73hH2416zz1tJ2qaQ004X4hTq28b3XbRhCclJTUhISFhL4FZ8EEzRcBiKy+XBlZ3iYKJOjfjdGAOl7hL6mc8swo4pVMUKpzTxtsQUdUCws9zxnOptuFQj+0DywzUE8NwTt0LWCKi2/g5y5igcqkBqpjxKme93ib7Zp44c27cXOqM2hlSR7NcIYNCI7Sba/XPqzrDxBNASqtVnpzVTYa6lo4wyWlVUHEDUFJxpICqWoawJKAljJwbZMOmY9XNI/ttvzOVrLaE+Bzxc33Yzj5mquqHbLwx6l54U9+WHNt3tRJYHxeoa+FCkPzcKV0f1VFgWbAD1/5jh+Am2ISrRnbWQlZhev+EzSraneuAs5pJfdRau1HYz08AOGmtfQQAGGMfBPCjAN637bPtLTwE4NnR42eha3d+EoDvA/BJxhgAPAPAhxljr7TW3hsfyFp7B4A7AOC6667b8Qni8uoSy889goXPvg2Lt70IS9X7sPzc39zaB/jHVtq6GQA4UCIvl/Fb+1Zw7S1l6kRN2HVcjH2piaQmJCQk7CUw2wkwCgqlEuADR9j6llygDQHqkxWtW6tueN7bgSmsJ1TbeHXUQHYUWC60VzbdsfvrU6e6BDUORiKC6hRUsv36ucfI5mtqCaNaO26cdsupasXwDkkNgT/SPZd7tbKdWc2gNcdkPMB4XHQqZozhqKpuqI9TCVVLWOFrXfIGohfaRDUy0quoNOdKM6PhWkXWW7L0xkpm/F4Kv36hTSCuYgMLLO0f7xdeQ2t7jmtp+goxhUSNxwMMhxPUpiWoRN6JoBI5jZXPmKjSfaMZuLCRuuq7VWuBgizCMen1xUTa8G3Zfc9hBc1XAbyAMTYHZ/e9AcC9p9/losIKgO9hjF0N4OtwoVE/TS9aa08BeDo9Zox9EsDb+gT1vGA0QnnrcSye+Fkc++qdOPq9h9xM6mUHT88i49qZa/xc3zdGvn7mMHCgxHP+UepETbgwcDH2pSa7b0JCQsIeAos+bxNBDY8jgtYno85CK6C9nZOei7elgKQYVG0zcy0zFDrA24V9fY1eL6DW82A9JlsvPSaCWo8LqGp2DQuAQFCppqb/GoEUy5jgaeVsreGcngxScu94XKCaFBiPB6gb93h9XOCJtQHGkwxVI7E2zlE1EuvjHFXU60l1Klnmeke5MJ3wohgxgSWSaaNAIiKodZ2F9ZseGRU+0Tl+rn/9VRRiFF8PuhFBpeobugbxrW4kqkmB1SfmUNcS4/EAWomgLq+PCyjNA5k8nXXXvQ8GoxmqRmI8ccS/roVTURWH0gxVHSUo++2V5mFedVuw56aCxlr7WQAfAPAXAP4S7vPUHdtb5N6DtVYB+McA/hjA/QCWrbX/jTF2O2Pslbu7uh5WVjC64wiWHr3HfYB/9B6M7jjipM7ToV874y2/d31iPlh5+7OoyeKbsBuIZ1BvL28P1t9N1StdwEhKakJCQsIeA4UbxaBgJCKqMZFzkTjT1S5GcVdjI/UUOWXCgMMl9Ib5UiKfPWuxgSfCAIAMHE2bKExkmOYs6wxWMzBPbigkyR3LpxRHs6icW2f1NU7VJWWStiP1z9R8yurbCf0xbSpu395rDA91Kn0L6kYdndpwFKIJqbzD/eNgxaV03ngNFKA0a23QHApySjkFEAKPYsUVAKyIekj9NaF50j55Ha8OO49npQvPUpEBN3MqhUZdyxDiROpyG3KkobQIll4ubCCk4f10FFYerq02zF9P5u9nKHINbbrbnw3sOaigcce1vwbg187N0S98WGv/CMAf9Z67bYNtX3I+1jQLo9d1Q2XKq0r3+HXLOK3OtEHtzIH7yjCDevx4+5NmUcnym5BwvhD3pVLSb5xavVeTfhNJTUhISNhL4HZqHpVxAws+MwQJgE/CjQhJTIYK1c6Q+v2J8DKpwTIDlmkwaJgq82FI7f5EZomo0nl4n0UDUTDSdK1MsPX2CIkxPulXceRzFbjvyzSG+XlVR6qMau2xcTJx5/xeRZyMB2jqDEJqaCX8jaNqMhjNsDbJUdUCSrNAop68r+nMSgLOalwUTkEdDCswPxfbIOuophuhn5xLjwfDie9u7aqofEaIEBMmPM+ECbOwTBgItP2sk/EgkD1677Rf3/LrZk3lVL9pTOCJdMYzuoGUNuiQ0KIXpCSFwaSSqCIngNIMyquXSjNIYSG4DT2ptN9WYQHYs89eStjDiD/AAzjr2hnqRb3xRqRZ1IQLAjH5jJN+D7/w8J5O+k0kNSEhIWGPYWoetUfK4tAcJkyr7GnRURulDx3aqD+1nyA8Ky037tM00fFJSaTHNIvKaXYxV7CagQveIaamR7SJoPYtvmH7iKDaMDPZJ7ru9SrYegto5ay6AFA37eyps/b6PlZhIUMgUJegzvlk3ixrOsqlkAbGtOFG/fnSPgnnwrgEXZ+SWwyqqde1r2aJO13dTw4BwIj2d0AzsojOSTO3dC2qKkNRNG3qsFeS6drVtVORdcNRZAoQ8EqpCETUEV4FeDJb111lVinektBaAG1LDZTiWK9EIKN9gir8FxHasG11o/ZxjmZSEy5wkKIUf4CPFaXt1M7gQOmIaok0i5pwQSJO+l28bhFL9y7t2aTfXSOpjDEBFzLwdWvtjX74/i4AT4Wb8Thkra0ZYwWA3wPwfACPAXi9tfYru7TshISEhF0HkUULHtW8dFNhgZYoktpptYauZDvTGHWb0r7xjOmUYptpcKNgJlm3RqZyFTFx36mupE8Z7tpwmbAQQkFEtt16XJxWQeXSdFRE49XNmNCqWk6ROALNeRJBdfOVHMIT3/VxgfEk66h/c4WG9K8rxTt2VSk09u0bI8sc0WM9hY9zM7PSxvS+TAidodqpufEsa6x8ay0gtEvWpS8d6kj91J6AkiLa1Fmw9AIIoVCxpbmKKoliq25ddz8WkO1Zx52u3GBtnEOpHFKaoIzKDb5IUJo5ourva8Mw8f8mlGoJpGQ2fDFARBVwZDV+vBXIQuCy/+Gybe2Lv93ebgkXAE6cwPz3SCz8l6g78kPvwMIXbsPyGz+yuWNEtTMn3lvipdeWOOgfj+4r8e53Azfc4GZSEzlNuJBwsST97qaS+ktww/ZP9o//JYDfttbexRj7HQA/D2DJ//yWtfa5jLFb/Hav340FJyQkJFxoiAkq0FNOI/QDjqYSfTVHs14gm6tC3QwvFFjWWjXZDKJAYUidICZlnEKKrrWYCx3UVFJGmbBBTTRhJvLMBLVz/saRsn5XK4AQjtQ0WSCoTikUYa53PMmwNnakjZQ8KQ2KTIVUWUr2BZyKKqLk4XANeyS08QFIca0NQQoz9Rf49J2nft+ovxSYVmbdtgKrT8wFUhyrpDpaIz1P16M7Z9qm6lK4EV0TKQykNFgbO8tukXWvgxStRTeeOSXFVPf+GQnWElTBW5svQF8QbH+oVE00vvnFU9veP2GPYn4e5cIClu844hSlp78CS198H5Z/+DTVM3GaL+BTfI8Aj61gfr7EyxZKfPzOZTz5z1dw06ES1gJvf7vbNM2iJlxIGJ0c4bf+7Ldw6NpDnaTfvTabuivpvoyxZwH4KQD/zj9mAH4cLikPAN4L4NX+/qv8Y/jXb/DbJyQkJFySCCFGnnDwQQP5lDGEJ5hiXxXmSvtgwiCbc5bSOGGXQpQozVfsr3ztjAUjxTUiiLSv9uqd0QJWR6/7AKH4vACC3TcGF8bdemsmIhvCkZTo3ryNl/pOdUjG5eF+U2eYjAdYfWIO4/EgzFtSuiwpqIEcUVqtalNlBXcElW59S67VPCQIa+WDmGqJpnHnr6qs0zVKfaM6+nIhy5spBZjIqCGiHd3itN6mce+x8WT98VP7sbo2xPq4QFVl7dpiW6/hkbVXwGjW6UHtQxuXvhuTVgo7Wq8EqsbVyCjFw7wpEVSluwS1Md1b/zzaMJf4G50DACbV9r5Xt3Z7t4Q9jLIElpdd9czqP8Sxr96Jxe/9WZQ3vWXjffppvk+bB+47Djxtng6Hlx0q8QvvOgxrgbvvbhXU5eUzhwUnJJwP0AzqsfIY7nngHhy53n1R844/ewcWPrCA+Svmd3uJm8ZuKanvBHAYruwZAJ4G4Ns+0hxwJdHP9PefCeBrgIs8Z4yd8ts/ev6Wm5BwfvDg/fdvetv8ssvw/S94wTlcTcIFCetmUcnmy6SBfPIYjFvYgsE2bjbTcqdk9q28smggCgUBhWa9ABe6DegpFHiuHFHdVwPCAERaGgtrmDt3VGFDaqeu3WYxCSUVNZDN6G30k3hDcJJfbz8kqCWq3YAkmj8VQkP5P2lke9VahHqZqsownuRYm7TDkUTKYuIoAaB2pEvKlljmuUKeTYdBufNw1JOirfLxpDLuGuU+dInmYGMyKESrysYhRtPnmbYMx+fQ3tZLRNxd17bDlNRSI7pdpbSdib5k6Ku+ijvCuDbOILjFetUj1Ia5a9YLW1L+mKo3F9r/qiLeT3XW4eZWtdned9MWgEkzqZcmyhKjxVdgaXwnjg5fhKVH70F5crSxkrpBmi8pq2UJLC5Oz6HSa0lFTbgQEAeFHXzGQdz4/hvxwme/ELeNbsNH3vCRPaWonneSyhi7EcDfW2s/xxh7CT09Y1O7idfi494K4FYAuPLKK3dgpQkJ5x9mbQ3Pv/zyTW37uUceOcerSbggYRlMNDdIaicyDdYIINPOxpoBzJg2sVcYQPFOtynZb0XeOFutV1FZoRxBzfyt4e64lYT1QUUxQY3BRLfHlIcqFeHqalQb8hNv0xJa3X1/Urc2YOVqZIjA9gmbECaojICbxVxbm/OkTfiuU7dP36IKtBZXxWlekmNQqED0iGBKYaZItNY8EPrQR0pBRgB4bjqJuu2adQgwIjR1mw5MVmgiqKtPzGE4bJXcupHQSqBusqCKkjoshUEBBQWy9TolVBsOoR1p7if4ni5Bl+ZJZ9lvlWaojLsVvdf7BLV9Hii81TdGrJ46wmpCwNJ2kFTRSxOjD70DC/X7sDw4hPJf34PSW39PGyLTS/M98d4S8/OOgPY7URMxTbgQERPP8uoSN//Dm3HnF+7EoWsPBYK6V9J+d0NJfSGAVzLGfhLAAG4m9Z0ALmOMSa+mPgvAw377hwA8G8BDjDEJ4CkAvtk/qLX2Dvgi7euuuy79SUpISLgoYW2bujuraiUoqb0+UyYdyQTQCTjiQkPuq8IxWaaBRsACYJqqaThsJV0FTWQPBhCRTtd9yqXpzJ6GWdhItYvnV7nQMBDgaHtBCcZwmLo9j2qy0KlKs7fh/Qnjrb4iJNieiaBuVG0yKFQIAqLAJKUFuGqVUKA7QxoHJAFtWFHtU3uF1KiqLJyrqTMUT1r3YUlROnBPUQ2pwP49GcMxHhehPoeeJytvXw1VOncJvXT8UB3TztgSai1P20kaq5mdBF44IhqTU2UZJLNTM6iNcSqqsoBkwEBgSn11a2+Pq2oBySyqbampLKX7XooYjbCydBTLi7/pLL7lKMyonrZ6ppfm+9Jr3Sxq6kRN2IsYnRzhngfuwaFrD+F9X3gfAOCeB+7ZM2m/552kWmuPADgCAF5JfZu19mcYY78P4LVwCb8/B+AP/S4f9o//zL/+n61N34smJCRcmmCM1FNHUmfWwgQ7cNt7Sl2oboa0Ve7kXA2xz3l1O8dqhCe8rEN8p84V21bzJqT2cjjiGAc0xfOrAAKJFcK412aM0cb2Xq3aOpdZPaRNnU0RVIDCf1qCGofyAG7WcdALkuo/1ooDmbfXcoPBjNnapskCkSQCGa8NABp/jYXUyLIGxbCamkXN86ajnpKll45RNxngldMYUmgotEnA9B6VyjEoFIYDXzmjWZivDdd1g25ZwJFdun6duV3dqp37ch3UTjdTOh2SNPH/vJR/bsCBwYxE4JigTjT9s2CotvGX31rApE8MlxROfPoE5j/zIA4f+WhgkKOrgJXfeg0Of0mhvGkDi2OU5ktVMwf/dAEfv3MZ199cpk7UhD2FWDElQhorqnsBuxKctAF+GcBbGGMPwM2c/q5//ncBPM0//xYAv7JL60tISEjYfXAbFM8+QbWGtaFKHRU1SseNns/mKsinjN3xomO54zDocQ69WkCPcxhv9bW9HlOCyBVk4W3D0kylCxPUOIeuz/z9KPWfqiYL78kYR9TiIKHOfU9Qq0mB1dVBe84QWtR2ngIIZLVq+GkVxM6aNiBzRkfnfmIO43HRmflUWqCqMtRNBiE1hsMKA2/bJcIthIbwxJ1mVJ2VV06dd31cYH2cTxFVen+xOrzRPKcU2tuYTadipw8V/ZvpJPCK9md8X3ALyeyUgkrkVDJn8+3bgt11cuSWCG7lb6sWWJ+e9NkUUnDSpYX5K+axID+I0VXuMX1Yn3/RLcDh08zgPbbSmUGlGdWDV67grW8F7rzTzaTGnainO1xCwm4ink2NFdUP3v9BjE6Odnt5m8JuVtDAWvtJAJ/0978M4IdmbDMB8LrzurCEhISECxy2ieyzhoGR8ukVLybdPKrVHGbiraN1Bi5NsPfynlo4fQ7emQ8FulZdoJ1B5UJ3VFOaP9XeYqx9pYyqM5/mqzvHtJpB1RKqlqjXi9Az2pBdVmhU42JKdSTyFiuYpKCOJ5nv9KS0WYsiM4iTdglVLYC8c+ge0XN2Wil0qLQJr/UCmir/mjR6ivzt27fuCepkisirYPHlwfYspAb83Cl1na6Pi2Dr7c+UAmTzbdNxXbou720jQleqNi7ll0KlqAv1TChyDRGdwx3XKamVYSG5N7b3CgAZd7UzfUwUn2kNJnKazYyoODNScNKlhfLqEsuvXXbVM9ctYunepc3ZG69xjPPECYQ5VBwoUydqwp4EzabGiurKwyv4gWf8QEdhvZBDlHaVpCYkJCQkbA2MW7BMd0mqnyE1vYoOJnVI4tWVhK4kBt+xBrGvBi+aUC1jVosw38q8uZKO31dOYyXWao5sroIoVCfZlsClga7kVI9qPKtKz6s6CwR1vDacmj0lu+tG6bbVpFUulRaOvDXA42sF1mqBiQYyCv2pBQS3nVlNXQtHVD2cKmj8rSXUSgtIAKtPzHUCj6pJMfW7Wh/nkMJgOHB26v1PWsf+/W4OtYjCj7Sv6yErcyCo/rx5pjBWreWZCGp/lpbSe6tGhveiOrVARCR5sFYbT2CpVgZA6ESNK2koYZc6TOM+VaU5UItAUCeKB4IJdAnqQEwHJZF6Ouk5qOOHGRgG2DrkQOBp/+Ap29gTbsgoYU+ivLrE4nWLOPapYzj64qOb70YF8NJrR/jw76zAlVAAN92E1ImasGcRK6oAsPCBBRy53s1m0+MLNUQpkdSEhISEvQRmXd1M72my4wLtbKn1Ex1EFEXh6mWcvdcGGzDLNGzlA5Aa4cKT4PtY4yCjKDSJnqe04LiXtbOPFlDjPNx3+/TSXGu3PiKo66tzMJq7UCG/TR2l9ob9vBW2qTOs9+y1hLWJI6iVBWCc9VUKG6y+gpvQk0o1KmRnVZqjyOLzcQhp3Hm0CKFIs0BkURuXoDs3rMC5s/GKKB2YSLcm23KTgXPTCWUixCrtRtDGKahxEm8IiuqpqWOvsK+Ns5nJubHN93SgihqlHUFt50j96/7QGY/36c61ht8RWkJLtzkwCDiL8FYdv2qi8ej9p7a2U8Kex+jkCEv3LuHoi49i6d4llFeVs4kqdaOSzfcbIxxcWwDetIyXLQDPex46nahA24maSGrCXkA/7Td2GZwx7XqXkUhqQkJCwh6CNQymkh3VNE75tYoHchqroNSPSttbwwDFgUa41F4KRfK5SqaSroe1l6JLRJPSe0ml5ZQYHBFYowWa9aKzHwBUqwPkQ6/q1RlUlWGyOkA1LlCNi6lQJK1FUFFJOQXaoCSy97rztGrhqdUclWGBMO2XjrAVuT/+jPnSmNTRcbi3+WrDoetp26zRLNhuSZlcG3e9w8Em7OtljGkDoVToNDVA45KBi8KptETE68Yl+JoemTSaodZu/6qRmFRkbW4JqpQGRaaC+lk13RTffrWMNgzrlehUzfRV1M7+ygUrVQ0PaujAv0y23jXt7LtC+Eoav11cTyOZU13JEuze4Mw8rU3DIs2XXhKIPLrB3vjcIyhXFMrXLW/8YXyDbtSDB8rUiZpwUWLTLoMLAImkJiQkJOwlEEndIHzIVK3SFltzSUV123hS5F8jgmoNbxXVnooWk09dZ7CawUBAVRlk0YTk2aC2duZWLaBduBBZWetxAaNEmEOtxkU0f2ogcwWrOWofjtRPuY2rVwQ3ENIAdVuj8vhagccrEeYinyKBfQONItcdm2w8uwl0038BeGJooOCIKpFSImluVpW3Hav+eDN/N74+JsubzvwppRUL0faoVlXWSfOlDtQ+lOZhDVVkuQ2BRtIE23KYX21aS2/83vsBS5To2wZO9Wy60Txq1XCsKrc/WXqliAj/WDqF1TriSuSUZlAzF56MiXH3KVRJsFZpzTi2xVjNNmdZE/YQ5ueDD3clX3EE9VYXxUvq0YbVM1E36lf2H8VVB8rUiZpw0WLTLoMLABdSum9CQkJCwhlgDYNeL2CqLNzCa0qE2VNdyTCPSmDS+EAlVycT72+NC1ii2ywYLUIQEll2reZQVQZdSTTrBdQ4RzMu0JwhxVfVsqOeku1ViOnAnqA4RgRVaRECf/pYG+c4NZaBoBJIGRR8c8FAYa2ae2LqCOqp1YHvXZVYG2d4fC3D2tjdqlp0yJ4UBkXWOKsvN2hqlwCs/QwqEdH++64mBcZjdyOC2ldRiaBOKom1sYTSrEMk4/c7vR/rkOm+3VdphlXlApAmG5BuUk/p/WZ8mqCGJGBmO4FJ2rYEVbD2NuDd1N+BNJDMYiBmhy2dEdtM9k3q697CiXwFozuOAAsLOPzHqyhvPY7RHUdwIndzd+XV5cbBML4b9Sv7j2L/3y3h9//VCAsLrhP1nnvaTtTR3ghETUjYEOQyeM0/eA3Kq1rr7+jkCKOTI5z49IndXmIHSUlNSEhI2EuwrLXTBvKQdR7HKmYnRVe1NmArTahLtYZDrxVhfy67Ft84/Kh/TKMFeO+cBFXLoJwazWG8/dgYFhJ8qVomkDhkbdiTV15JRQV82q3yf7y8o7ZqMkijUdfSz4EyT4oYJgZhrpUIVV/pJJIViJpXGdvXHRkU2rhQooaH/aped2xskZXSYDhognVX+LndqsrA+cBfCw49Flj3+9CMLc2fkmo7HZI03W26b9gGQRGBlNJ01g+4JGOavQXaVN1Y3WyMmxHV/vpp2/ak0jG6ZLztT427VONtBItDk2Kb73T3Kh0zXM9ten79KHLCRY75K+ax8F8WsLz4CpTHjmF09BAWHjiO5R84QyBM1I161YESn/9YiZf8zQLe8jPLOH68TJ2oCRcVKEQJaAOTll+7jLv+6i588L9/8IILUEokNSEhIWEvwbKOjRdoyamuo1qUSvq+zShJN5pRtYqHvwBm4pRQIppWd9N3m/UizKCKQoELDaOFO68W4TUA0H4+0mgOVUtwTzKMJyvGV6mEdSpHUIthFRRTIqpaC3eL5lABR/YENLjyFtuxCATV1a84RbEyLCTKAkTc0FH5YoKqTXc2MyaqgCOrVS2wVgvXA2psR70U3GLg536V5tg3qFFkLUHlvE3Fba28jpTGVl6tOMaTPLyfmKDSPGhMUGmd7dxpHt5PVy11VuS1iYCyjshTqq7+/9l7+yBLrvpK8NyPzHyvqloS2HIDHjTSYhajwTK9VhkcAnYyMF7w8rF8vWGMe23GE/aW7VnNAtFY8hYRS8cipi0UIc3aZSvGEEbyjPzMhw0EYmFm34THHguXsFg8ox4vwmKwA5AxQbe6qt7LzPuxf9z7u3kz36v+KHWrVaV7FBX16r38uJlVrarzzvmdA09G4eZCAye0AJgnq6obMKU0c/bomEz20CGyERmFRlhDbAsWnIXvVdzBGoxfDS4YNtl9DzzK60qMf+AWjL7wbqy99+XYqO7F+AduP7eNsdeNeuQ5leQzAAAgAElEQVTVJT78gTG++9VNrK2VnU7URFAT9jtiN0EcoEQE9alm+01234SEhIR9Dqt5h6A2OwWaaTE3G7pwX8WhdnI0O4Ujqsrbd+ssWHndsdjCY7nwpHkS4OZNM9SzHPUs9xUzrmamT7KFXGTxbVVUYL5uJYabFXW217hGpg9lW/tqbFONq1N2s7YCaEOJvOrY3zZODJbCIM/VwmsDHDmdTgtsnVnC41tLOHV6CVtbA+xMC0xnObZnebie7WmGqpF+rbzzuX9udz9MIIOx0ksKKhHTLcVCqm5lgR3/eea3pzuprPuY6VahXZQGvBtiMtuu0c5V0dDz4fUe+d2NCJ8Lye77NMBkgvLnb8PaC34ax/l/wNoLftrNpC7y6D58wimoAHD9MZz4nRIPfXYCPHwCkwlw7IMlvvWMY7jjjmTxTTi4iAOUXvMDr+kQ1KeK9TcpqQkJ+xRfPXnyvLfNr7oKP/TSl17C1SQ8WTCGhcRcQp+M0iyo0QICClzOd5gCrbLa3TcDF9rNTEJC17Jj79WVhBFtOBIR1PgYqpaoZ752RvFw/nAMv23YpuZg3ATrr/WzmqSiAk6JjFN9lRbQEUmUwgUeaW9Djbs6dY9wSGbDrCQRVbK8LmemrWxRbQgSqYczxedmI+lY7nGrajqyqsOaOTcwUkN7NVlpgarJOnU1BVRXNfZrVIpDChcSRSpqIKw+wEgKE2ZUCY6kynANSrfEVGOxMJnBEVS5gIdSMi+poG4diwnr+ZDKvs03fuOgOHvbznkj2X2fBth0M6kbj9zWBsLcfQvKRR7dXu3Mq26Y4LlfH+H3HxnjF/8PN4t6223tLGrqRE04iKAApaM3HMW9X74XL37Wi/HOH3tnm479FLD+JpKakLBPYba38SNXX31e237x29++xKtJeNJgWegdBRw5NBGp4V5xjIllbN0Nh+nNtbZWXxYsu/S8yFXna0TktD0eAxMWRgvUO0VHLV1EVFUtQ6otfQ04AltTUrAnY1neQAhH7rZnRaiciQOBuLCQwmBbuwAhbVvrajyXOlggtGrDQnpskZmOZTeuV6GZzUUKIEAKpvFr0RCy24daNxngE4y14h2CSucTnIeu00HhiG7VSJ/c25LT2MYbiHYl/Qxq93uz3VOXtf/YgcWMWWTW2aIzMGRwfaSit/3Z9XgE2zORTSlsIMX0Nb1O64uV1ZicxgS4e38vXN6UA4Hv/cErLng/AMDm3nZLePIxeetqp2amvLZ0X791jDl+2audObK9gYeuGeNn31zizW92BDXNoiYcZMREtLyuxIuf9WK8+3Pvxpe+9SXc/8j9TxnrbyKpCQkJCfsI1jAoqmWJgogIMleQnlTGRJUQE8ZQF0OBSpGl12geCG/n/H4bIsbcz73Sa/VO4WZRo3PoWKn1j2VkhdWqtfVS2FI8iyoMD2S2qlwdCxdd5TKGsiwk+1L3JtjuRGumeCd5NgYpqJVhIXU2nkEF0OlV3Q2k/LYqqEvrrRoZ6mAABMK6PKzDvlIYVBCBmMa1Mf0uUx0RUlJ4VS3aChe4+9HAkVJNvaRgGGCxekpo958nme6a2MLHMRYFKsXbx72p/df3oqw2M41vn3z8wndM2FegQBj6w/pCamfwonUcuaHEu94134uaZlETDiL6/17e+WPvxJe+9SXc8+V7nlLdqYmkJiQkJOwjWMugKlIaWSCqXLaE1IUlWQivCOqq+7/6uPPUWXYj1bNnCQbQUU2JoMYkOT43EVRSEOm3TDUtIKQB888LoQFPPBkXYW6VwpLicCWqYmnqDNNZHpRKpUUIHJrOMpzeyvF4JTBbwBmV7+d0ZI2hqgQKr45KZgM5c4E97f0i8rTcC1qi4CUAoW+0vUcMCgJCGRgRBz4ZQLkAJik0lKfNRC6LXPtjK+S5DpUzRGx3KgHhZ0yLzJxzNpTIc9XwkHQMOGWU+N4ArENMieLuFqZLPaekdMZBU8C8fbcPIs47lViomlKYk7IM2zOBInPKsLLsrNd6NqTx0oOLE39yAqvPWe0EwkwenWDzG5s4dtOxXf/Yvu/OCd50eAP5i9aBr2zgoW+UuOuuEq98petHTeQ04SCjX8c0eXSCj538GI7ecLTTnRr/W7ocSMFJCQkJCfsI1rjUXBdA1CVzQGv3FXkDLrQPNpr/X73RIvSb0mxpTEDdsXSb5KtFCD5SVRbWoGoZ1FOy7HJuwKV2H9x0rL5CaBTDCktXbWOwMkO+VCHzFS06OjehqTNsnVkKAUNcWCwNKywNq5CcW/twoe1aYEu3QT8Ci9XTmQG2NHBaOSVxeaA7wT6U8hurfUVmsDxUWCrctkWuMSgUBoVCkalgEe6DSLQUOsynEtxzLZF0c6cGea5RZA2Ggxpc2Dm1OA4X6q83Xjetb3mgg0pc+fuyxIArBbAigGX/MeCtWhrW7+8lsHtPaXzOItfuoxeUBPhqGmmCaqwsC/PCi6y8yjJs1wLbDQ+hTXuBsXv7SHiK48QJrP6tDD2PADD5xB0YfeR1WH3O6u77PTbBmw6PMLpzjMl33oeHlsd47n8d4WXPn+BXf9VZfVMvasLTBWT9PV4ex/2P3I9bXnYLRh8d4Y4/vQOjj47O/m/pEiMpqQkJCQn7CNayoFaGXtPc/dHvrL4N2II/+K3mYTZV1/3KGeZJqGzVUb9tbP8l9ZQUXCLIRnHwiCxzbiIrsrMTN02GpgFWPLEMrysRLMEUlkS9qJSA2+8LjQOT6lrg8e0Cp6cSp1WbTDuIrl33PgPO1irgiFdfoaOvSYnsk8s+SOkkMskDeeQQMJ16me55BAQ3QUElwltkDYTsXiety81+RkFWvblPwW2ob6H7RTO6RDZpJpXU1H6wVDhflPRbnGU7WpvgtmNdpjXtBu3nhbGgIobOReFXArtbgc+FxDcPKFZXUY5GGN/t/qhe+97XYOMv78X4JeeonvnOJvJyjJsPlxiNgB/+4RKD02Pc+d5NPM/Lp+NxmkVNeHogtv4eedYRvPbfvBY3PfcmvHfyXnzqH3/qsiqqiaQmJCQk7CvYzowngXOLfFgFgkrpu52e1CgsKU4ANr5qJhxLaqiqO/eqajmv2nIDY3jH3mt8Qi8RT2s4tLcXM2GgGkdESXVVTYamlphuDTGbOmqpfPXMdDoI4UKAI33a8EC+qkZie5rh9FRiW7fEa8eXe9K8ZYOWkAFOMSTLqrKsE9rTVynJyqoN8+FFnijVAor7ehxp5kOTeDtvS2snqED8md+vRpGRijqvyLq+1HMTNOoVrRoXlkR9rluKzdl3VSCI3edizAA0nuLtWIYl01Va+2varlv78m6gntfYIjwzgGDe2mxbYtrH2UjybrBI6b4HFmUJjMcoRyOs/eILcXznHqy/4CjKN77z7Ptd7/7QLg8Da2s0h1riea9rGWmy+yY8XRATz/K6Em9+4Ztxz5fvwdEbjgaCernSfhNJ3Wf4iwceQH3q1Hlt++jJk+ed/pqQkLC/QEQ1KxpwqZEvVRC5AhNmzt5Ldlvtiav2naWEepqDe9IQz5ca5WZF4/AjIXQgpVxqQLUpvFxqmLolqNPt4dxaHGnjqI1LKFZ1hrrOMJsOUPvQIKeiZtiZFp30WwCd2c+dyllBt3SX1GRgaGBDOBCpqsp6e6ufJ427RPuEK1hTPcFXmqPy9TZ9SLQqKhFUCoWqiYz7oCRSWekxqaKU19y39hrNwjXH6bmLQEqqI6ouPGrHq4/0tkY8cxqTvgaOkC55VZO+JuzAQliX/qttN2CJFM5tDaAWWM71XMfp2Ygr4GaFaV3KtuopKd59G/KFICmpBxhlicnaa7AxvQfrw5dj4+/uR/nopKukPnzC1c4cLnHiBLC6CpTXT/DVP9vExsYxHD0K3HFHIqYJCZNHJ7j/kftDLQ2Ay5r2m0jqPkN96tR5E8+vPPjgJV5NQkLC5YI1HDJrIPMG+bDu1MQQyA7MlOk8p/1cKYEIaz6oYZQIRLWe5ah93QvjpkNQ3bG6im5s3Z1uD8PzLEoJbnwFi9UctU8pbpoMdSPR+HUoLVDXMhDUmCgSYmJJdSpEsNxRFtepFNwGa6o7ZpsqK1lLArVhkNE5leJhv9jCqjQDahGU1GDtjb4d7nr8bKrX9fqKaUxsY/XV9adG6c27zG5qCxS6VYRnBviuddsuLbDTCrRW3gbWdaYyG9Rnuq/xvZPMqZ4DPm+9jfdz19OGSrlFdbtd42ui9GVaF5gn/kAndXkvSiqQlNSDjMkn7sCovhfjwVGUv3E/Sm/97fxRHfWirq6WuPPWCV7ySyO869fHqRM1IcGjX0sDoKOoXg4kkpqQkJCwn2AZrOFg3CBfqpAPa2S+rsRo0UnDs5pDaw5dSTBh3CxqLTHbGiw8NFXZGD+b2lQZtOIohhW4NJB5ExTXOFm4qTJYr6ZSnUxGs6eC9/pQfU+on3FtmgxVlWE6bdfkCKoMBFVw4z9zAK6yJcyLGoaZ4WgWKIYFc72oizo3+8pesP5SSBB35NM9dnOe2rBOqm8MR74k0ABFxgIJVVpACg0jWLgeCn8CgJ1pEQiqFC5oygiOqsqgtMCsaq2+lC5MM6ehb5RZaMvwnalAxl0o1AyOZA7A5sKjSAXNrCP3sQo9YwYiIrXC39dD/jgazo7bYF7dHAhgIF1gkuz14oaKHW+ZnimOxnQtxgJtj22cIhzP2u4aO7wLLACbpNSDickEmxvrGK/d7iy+5STMqHaqZ6Je1PL5a7jp5g284cQYV7+oTJ2oCQke8WxqrKh+/OTH8Y4XvyMpqQkJCQkJZ4f1pCErmhCUJHJHCHdL8Y2DkdyHI4qkiPaVUZopVbWEzFUgqDJXUaASR+3Dk2helY7LhEE+cMTZGg7UnSVBax5CkqoqQzUrwmtEULdneSCouQ8WkjDQxquqigdVccCBSrf2UHpOMEeadrOd9hVKpahflQENhza+Z9STxKVCd1J8leZA1FnaKr0+5Tiy88ZYGlYoBhWM4VhCS2Q5NxDeQu3uxby9mMjpbvbZv9PAbIHBNfTF7oKGWWhYTJlGBg5pGQTcLOshsED4OzOj0YxqwW1I9C3yNrW4jwpuVnamXdJwDCK5QDsfHCvX2viC1wtANhD4vhdecWE7Eb64t90SniRsbuLYLZ/ulpqOxyg3N1G+sRfwEvWi5i9ax+pry9SJmpAQgWZT+4rqO178jnl3wpOERFITEhIS9jHinlMipG14EnO1MVUGY9zjpsqQFY2zAsuWoIbjGR6UUSFNUEABsvM6OzAR03paoGlcJQ3NreZ5E9TefkItgEBQyd6b5Q24ciFMcqg7c5n8LOmwMZZYq8LFiK29cWXLIkhmkfFIRSTiJQ2KTHWIlwv/MQAkNFl5o9erRqKACl2owTaseVCb3dfdRWtP/Ksm8yFR84S03y+qrCPXp60jqKSGZpEll+Y8AYQhzQZobb6eoCpYKGgcgsTAMmSREqt7qicRVMFcEjLdqz76Nu2ZnhdEhT9+bL/ub7SXntRmpvGtk49f8H4JT11QN2p5bEE3anlsjmmeOAG86oYJjmxvAC9aR/3wBh76bIlXvrJMnagJCT3EiirgwpTGbxl33QlPEhJJTUhISNhXaJmCUQIKTtXkwnRqYgAEYkqzqarJwEg9lW0ab5zMSwSVbL7uPBy1Kjp9p0ZxVNMC9awIyijgqmvqOoOsqfu0S8LqOkPTZNCqJXYKACQg4NTELHcVLHHo0CLEBG5F2mABJmzXwpE544gqfZB1tw8iscqyQLqWh3XoLaVaGG04uA9C0sZAcY4iU+F6AErk5WEGNU731b5ix/j7rA2H5rwzk1rXIpC7Gc2oehWzBUPG3Zyosq2CGtt1Zz5Aqg8NZ+1VkRVa+f0lGAaWI/NzvbQ9Pc64U07pXklmsTxU4R7Q9YfjerW5ajhOVf5nMSK8pH43UcovHRu6JadbKlXQJACrz1ntKDvnSh991Q0TPPfrIzx0zRinvlPijjtKfPjnRvjrvz/GqaJMs6gJCRHitN/whtB1ZSCoT2YdTSKpCQkJCRcJjLHnAvgIgGfB5bXcba29kzH2TAC/B+BaAF8DMLLWfpcxxgDcCeAnAewA+Flr7Z+f/RwIRJNAFTJxf2lTZQtrY/JB3SGlneMoHgiqXBDEFM+eNrVENS063aYAUAhHbKtpASHMfP2N5oGgAoCQGkJqVFXm7L1SQysBrTzJVbsojt7yqjQLab19kqqNm30MgUdEAnexy5LiWvCWoC4NaywNK2Q5rcclD6MGFASENhgUKsyaAm2vqgs9ahXUNgDKHwPwaqmvbqndtoGce3JHfaKxGkp3o9JtcBQhswxNRD5JLW2/tlDMYrpgwFOCYWiFU0rRTdgF2jlRutcFHLkvMoU81zAheMpgVrWhT1XDsd3wuTPG6b2NcSm/sSJOAU2L7MHng1RBc/BAys7ooyOs3biGjQc3zmpFPHLNJh7CGD9xtMQP/zCw+Zcl/vrvj3Hkmk3g+jJ1oiYk7IILfUPoYiOR1ISEhISLBwXgXdbaP2eMHQLwRcbY5wH8LIB/Z639AGPsVwD8CoD3AHgNgOf7j5cA2PCfz4mmylwNjCemsd1W1RJNkwViKPNWS1O1RFY04RhCaBi0SutuBJW6TY3h4fFsOugQXQpLou3jwKTOc1HNTZY10FqE/kyg7Uk9G4pco8g1lOIuUAeOfMaBQoCbcSTFVQXCOz/nCRDZcqRzOGiwNKyQZ00gqACgezFElOoLtLUySjsVNE4Gbs9lYDSbU4npa7p+1wnbBgxVUciRI5A9tbQXDqVhQ2Juxbo0bQoNxQwGViADR2F5uw+AgeVY8jbfgrXKadwdG1TnQEo5jF5MB5VmYbuBvxU019qvl6ksAN19bmb2RlAJiaQeIPgOmbIssXbjGo7/0XGsX3MU5e9vAsd2YZnXH8OR6+NOVODIq0sA3s6Y7L4JCQtxoW8IXWwkkpqQkJBwkWCt/SaAb/rHZxhjJwF8P4A3APiHfrPfAfDv4UjqGwB8xFprATzAGLuKMfZsf5yzQgjt1Uo9RyDjACUmjA/kaedDqVYGAFSUygsAMlftzKqH1gLW8M7xST3VypHOkOYbWY+1FmiQdYgqEb6mds9zYdz2hgdi19Qu2bY/z0qkrz8fuj3LQ2psXCETByYpzbBTifC4T7hIaS0yFdTTYlBhOKzCPW7qDEZqoGnre+LeU/qstKuOISW0JcGmJeP1/K/ftp6FY1ZJZ0u2jqDR/Cj8Zx0pp33bbhPRMgULxeZpmrQcK9bNnS6BoUFrFx6AUoPbMKN+9Q6BFNVZ1b0epTmkNNBR+JOI3xvg6AQvxdixQEHNNZF6fIGZSQHJ7nuAsLoKjEaY3H0LNh7ZwPo1R7Hxl/eifMnt6PzZ/PAJ3Pf5VRy+oURZApMJ8OXPT/Bv3ruJf/rBY4mYJiScJ8rrojeEXrH+pM6lPoF67ISEhISE3cAYuxbAEQBfAHCYiKf//H1+s+8H8NfRbn/jnztvVNMC0+0h6lmBauo+dERShdBh1lHIlqw2tQwfM09aKSiJcxPUVCKojVdqqdbGaKpJcTOtXLhZUiKogE8JNq5mhlReIXT4oK8BdCzItVd0+yBiStZaLizy3M1CEhGk2Ue3ne0kxMYktj1mS1APLVc4tDwL9t6icCoqfcQKcB+0HiKoVS1Cl+tc3U2kGhPCfn6WleyxpDiSWhp/zJgJBLWBwRQaZ1iDGdPYYgpbTOEMb/A4az9o7vSQlcg8QRUABnDkdBD1yw54l6DSTG+R6/BYShMSj11dDp+7tnDdzIYPwRYTVMCR0S3rPs74mdonQlDNHj8SnoIoS0zuvgWjL7wb4zOvwfuO3Y/xS27H6JHbMHl00m73Pat40+ER7rx1gjvuAO68dYJ//Usj3Pe51dCJOpnsfpqEhASHyaMTbDy4gfVXrGPjwY3uv7NLjKSkJiQkJFxkMMZWAHwMwD+31j7OGNt10wXPzQk/jLGfB/DzAPDs4SFYw6H87Gg9K1DXWVAuAQA5gtU2/p88Eb/GK4FESLNcuQAfxcG4APc1L6TQknWYyG9TZ8HqS0pjIKakBmrh5zclODcoiqZj4W2aLFh9gXY+tqmzECYEICT9SqGhIFCga0Umu6yUJsyakm2XQApgf2bVHd+9tjyocdUVWx0iqpVANSvCOqsqQ1NnqGsJ5YOTumtpCSqdLxBmacK1tIQUIWwo3k9phm3/OJ5BJaJGKiqRU1JLVe9Hp2EGDQwycGSWY2D9vYKrl6Fjiug8MUEt+LwGOSiU763lnTlbKQ2qWoSkY7qvixJ54z7aGI6MA42/Dgp3WvYhTs0eNVGbtNQDhc3vUxjnP43y+D3A+jrKN74T40ePzHWj5uUYY4zwwT9cw0d+YQM/9etj3Pz+MnWiJiScJ/p1NOW15ZNaR5NIakJCQsJFBGMsgyOov2ut/bh/+jGy8TLGng3gb/3zfwPgudHufw/AN/rHtNbeDeBuALj+ymdZUj5pDpWSdeOkXoBIZjYXlGQ1DzZgsvcCwPTMECqyodaz3Cuz7Rym0U4ZDeRz4EjqbDrobmc46ijhlcKS6LOQ2hFVNIHQGh8iRGFCUug5sqrQJYfx46DuedUvVjBp1jSG0tz1rUqDq67cxhVXboW1E8FGIzGdFtBKYDotUDUZaiKQEemmY1UND12hMYiMdupYiJT6/YjQKjtP7DI4YtcwG9J7XV2MxYztrvBm4DhkshCIFJ7353DEL7pPEUHt23uJaLdzuLJDvN1nNme1jgmrsgwzv9xYSW2MU04JRFABYMbsQlJ7vkgU9WDhWL0KbPyaGy71HTJlWc7/0Xy4RH79Gm4xx/G+j6/jhleVqRM1IeECcLnraBJJTUhISLhI8Gm9vw3gpLX2juilTwL4GQAf8J//MHr+lxlj98EFJp0+1zyqtaxDTmN1Uisx9391HYUQxSSSbMBZ0UDmDYwSkLmCqmUgqtW0CIppTHSnniQXReOtu84m3DQtIY4TfGMQcc3hiCrNttIsqlY8qJQKAkUUxhT6UwXmlMx4RrVPRmeVxKDoVqTEZLHIFLK8wWA4c+uh1xoZ5mN3pgXqWoRqFQpDisOaujbjsxtGiZiS0rhdC8x8YJDocdRYRe1jN4IqwQCvnkowrHh7b3xMCl9qYJGBYeCfL/i8Eu3IvwlWa6NZp89WCoMKrRKsNJtTrklBpcCkJppJ3ekxyYbZds7W82gNe8GMMxsIPOuFV1zYToQv7m23hEuDE39yAqt/K1H+/G2hM2Zy5Cps3vZaHMOn51nnYxPUD2/grs+u412v38DbN0pMyjKR04SE88Simpm4juZSI5HUhISEhIuHmwAcBfAXjLEv+eduhSOnY8bYzwH4OoC3+tc+A1c/8whcBc07znUCa1kgjlWVLdwmtty64KIuWZB5gyxXocrGqK4CCgDKW3pJpdWBgLRhSVWVIcsyaGGgtVMaiaQ2dYa6yZBnDTREIFnTaeFnZGfg0ewqnYfAhQ3puvGMquAG2vBg/62DvbSdVx0OmlAD4zCA0hyCd7tKya4qhQkWZloPWXurKgvq6fY0j/ZnTi2NOkBnirt5S84CCa5qMaeqVo0MIU6k9g6kgbZuBlUIN78Jb8mton3jwKQpNAZWhGCkuOcUAAZWYAgBaRkyyzq5xEuRepr581BQEpHLYJP26xd8nniTmuzmUVlnv1jJVtoR1C3dklTJAAoEjpOLKRiKrMp03YpdOEltZhrfOPn4he10AWCMXQXgXwF4Edzq/om19k8v2QmfQmCMvRquQksA+FfW2g/0Xn8ngH8Kl3r+bbh781/3er7V56xi9G9fh/Hd70NZ+jqMR27DeO14x7t74oTrRv0Hp0YY3eksvv9fVeKeXxjh6K1j4P2JqCYk7AckkpqQkJBwkWCt/WMsnjMFgFcu2N4C+KULOodXMonU0Qwlj2yXs+mgM1s5mxYu+Mf3lnJuwLjppO4aw0MHqtYCs+gcBFI7433oXPGalBahUqUfgFQ1GQo0rgsVYmHVjBQaghsMhzPkXuXc3l7qEFVCHVEvsqIuDStIoTvVMWe2B3P2XCnaOdFTp5c7Vmkiqq5ahXX2JYIa1NLoGivDgIZDinZdSnFI0W7z+HbWCXoiuBnQxT8+jQ9OInWxgYEEQwaOxjJHUKO5VGk5DlkJgZagknJK/afhfkddpQNpOsnHNIMaoz+LS0nEhPjaiLhWhmFm2pnTDAyZpWvzll44K7OGxTRSiBWzkAss0OcDi0tu970TwGettW9hjOUAli7t6Z4aYIwJAL8O4FVwYwubjLFPWmsfjjZ7CMCN1todxtgagBMA/tFez1leV2L8P3/K1WFMTu1ah7G6CnzyNzfx/1zrCCoA/MTREp+7Z4wP3rqJj20mkpqQsB+QSGpCQkLCPoIxDNWsQFVlENJAkhVXdokq0BJYCkpC3jhiJne3omotUE2Ldp/OuecJJc1tki22rmWn8zOuaOFBYXMJvoAjsUXRhLULaSBgIIVGnqlwDXnWoEbmFFRKCu5lsCrt7MF55pJ4c2/vzXMFbLdzo7H1N7blnnp8paPABttxlFhLxItKfKQwUIoHYtYYoDEMmAkUmZmb6zy9lWM3SGGhLHMJuIK6V3ylDlzfKQUlAQgqKZjrcyEzs7QcQwgMLA9kNPOpvaRgEmKySjbfUJkTzaAuXq9TtWOCGvenCu4SlYmgbtk2pXgGG8hzrDPTNc6YxiGbQVoG5cl5s8fMXcsuDU1ljF0B4BVwPciw1tYA6ktysqcefhTAI9bavwIAP7LwBgCBpFpr4xjQBwD89BM96a51GA+fAL5nFThMBPQY7rx1gh969gm8/f88hvEYOFK6btRjr3uiq0hISHgykEhqQkJCwj6CtSwofMRKJOAeR/9Hj6cyY1XVapfiK6S3VXrCZw3HdGuIus5QzVxQEBGzuDIlENXoBHWTBXJaNS5MJ/cWUVLdlOaQMMhzp8zl1KsquKuu6al1sZqDFDMAACAASURBVApKXwfVWJmwDjnsWmnjmVhiCy6Ntw1JKjIVCHO8RqMZaj3f90mdp+5rFwxU1QLI29lSmimtrCOByjJInzTsiG2s4vZTgXtfWwZoP8MJZ4WdRZbekORrAclYIK0KFhIMEgxFRFAJRaSYAo5Qh/vLEQhqf6Z3N3BhIbTppBjHPbVEULe0q5IB2vlaDQsdkcd+UvHAChSWI7MMM7ianX568fniEtbJ/DdwNtYPM8Z+GG6K9WZr7falO+VTBovqs15ylu1/DsD9T/Sk/TqM8lo/H/c9q8Afj4CXjR1RvX6CH10b4bX/Yoy1tRSSlJDwRHDiT05g9TmrHdfC5NEJNr+xuXBu9WIhkdSEhISEfYhFaiLNisaKal8F0z6dF2ggJO3HMd0ehhlUFYUtcW46XwNAVWUofCKw0gJbWwNsz/JOjykFHk11PtebWRQt4cw4qbtdstlftxQG8M8p7mZIM68M07UCXbW3DT3KA8ksMoXhoCt2VcjmLKzUVwp0Q5La19sO1O2GY2YA5cXP+BcrqZKU5OtClTS2Z5EdOLKySk/cKGSoss4K21cSG09aJViHvEnLMbQidKACjhRSKNJAtOcQzCcGm1ZFjVXm+OcnVsQBr04Dru7IKGiThftBKb40g3qmRy4zy6CZs/QGgt2r0MnAMbDMWZQtxxbTZ00x3g3Uk7pHfC9j7MHo67t90jZBAvjvAPwza+0XGGN3AvgVAOt7P+W+wXnVZwEAY+ynAdwI4L/f5fVQsXXNNdfsesJz1mG8bOyI6vPXUD+8gbdvjPHyN5cUAJyIakLCHrH6nNXOv7X43+KlxC5V2gkJCQkJT0VYsDAnqbTozHwusuMuPIYnndSNOt0e4szpQ9jeHobZUq3aWpj+cavGBQpR6u3prQG2pxLb08yrqF0lzgUduVRYwd1MLH3sRlCF1OH5+DG9PhxWHbIbkmf9cQl1Ld1r0hGwpWHtLMWytT13Q5YcQZ1Vrrc0Jqh9xbNqOLYbjtPaEVTJ2goXF6DU/ZtdddKInbU3JN4u+PO+MU5F7dt8CTtM4XHWoGFuPnVgRZhDjVXUDG5tpKA6omwDWR0IYHmgF6rMfcSvxbOqi2pmtjRwChazBXZbxVx1zoxpnOENHmcNdphCwwyeYfMwg0qm8yk0dtj5Kbx92D3+B+DvrLU3Rh939w79NwD+xlr7Bf/1R+FI69MB51WfxRj7cQC/CuD11tqq/zrgKrboHl999dXdF0+cACbONRzqML7mno/rME6cACYPl8Dz14D/dBx3fWYNr3hriZUVFwQ8GoXDJCQkXCDo39rooyO8d/LeJ60r9UknqYyx5zLGJoyxk4yx/8wYu9k//0zG2OcZY1/xn5/hn2eMsbsYY48wxr7MGHu6/AJISEhImIO1DHUtwb0iB/jZSeoYjZJ6pTALrbRa8zB3Ws8K7GwvQeluzYzxc6PT6SB8NHWGnWmB6SzDdJZja2uAM9uFP5fF8rAJNt8YgjuCWmRNIIYhRVj7gCIlFqYVx6SzT1zPBjpenivkucLyoMZw0MwRaFofIRBU31u6UwnsVCJ8TQrqTPFgZS0YsCIcOR34ChlKyNWmVReDyqhbwktK5szA2YUNw5Zi2NbAaQuc5joQVAnmZ00FhlZgycrQg3rIZjhkJQbeIkuJvX3Lr+wRRsGA5VyjyHUgqFLoQEaDQq2Zs0P7Gp6daY4z2wNMZxm2p1l7j4xb+45XlRsfiBS+L/45UoUbT8Bpm2cYN7OrmMU2Mzjjrc5neNMJU7oQmD1+nAvW2m8B+GvG2Av8U69ENJN5wLEJ4PmMset8YNTb4Cq1AhhjRwD8FhxB/dsFxzg3VlcDwzx20zFHUEcj9zzcH8/HbjqG1VXgzltd5cx/fHwdv/gTG/ij359gddUpqOOxCwBOSEjYG+J58LUb1w5sT6oC8C5r7Z8zxg4B+CJj7PNwwQP/zlr7AcbYr8BZZt4D4DUAnu8/XgJgA2efe0hISEg4sLD+730pdCBcpKZqxWFEO0fqEn3bP+xp/jSuqDG+Uob2Qd6gmhUdlZaSbWvIMN85qwaQ0rQ22lz7kB3d2QcA8lwHgip7icKxNZm+VpoH27LoqZzG8M68qjFu23iGlgg2WZLlUHfIaUzuCVxYXzOTBSJaGRa6POkzdZiSlVV65XQ5o+twc6gxSaXPMTklBbUxjrgp63457mhnz23gSFps8R1aAeFnTinNV3r1dLBgBpXQfz5WeAtusDx0Ft881yFZGegmJ9P3iHpiZ5XsVO/E11LZ+V7XfjgS4KzJYDoQ1GeaAkMb2aCZxRbTOMOaPYcmoVVFLxX+GYDf9UTtr3AeNVIHAdZaxRj7ZQD/N9yP2Iestf+ZMfY+AA9aaz8J4NcArAD4fVchja9ba19/QScqS5z44Juw+r/9TyhffzOwsQGMx5hcC2z+yYkwD1deP8FNN7vKmRteVeLE50uMbx4hv34MoEx234SEJ4hd58EvIZ50kuqL6r/pH59hjJ2EG8B/A4B/6Df7HQD/Ho6kvgHAR3xVwwOMsasYY88+V+F9QkJCwkEFhQ8RhDSBUO5moY3RNFmYX90NWnHfJSownWXhfNpwZ4PVjqAUmbPSAu0cKXWZxiCC2g9EAuCtx931aCXA83lishtx7Vxf7XpVQzhT9Hrf2hujamQgXRSCpAEI3X4uvG125pcm0BJXwectvgA6xDRcn9+MCKqGr2LxymE8g0oqagzpZzWHcMQVaBN8gTYkiZDxdu60yEwgz0Smz4U4QIoIdxwWRaCKGcLAMsyipc96xDuzHA0zWLISEixcCxHXM8xZgfeqomYDgee88Mo97YsHz72JtfZLcPOWTztYaz8D1/UcP/fe6PGPX4zzrL78bRj91b0Y/85xlGvrmFyL+Xm472wiL8e44eESx48D6+sl8nIMfGcTOJzYaULCE8E558EvES5rcBJj7FoARwB8AcBhIp7W2m8yxr7Pb7YoQe774YludKzzGrxPSEhI2M9gzJHARTUyRdEgy5sOEdRaBFJnNIfuWYJjBbL2VTI706JTvzKr3K8KUk4pyTUmZJQAzL06GauUUjiFLrbuEnEkggq0BJsqX0RELkllpWsTQrfKcG871XueVGEhTXiNSDgpw9pwPL6dYbvhaExLvIjsEWGtLKD960tnqe6sGu7mTr3Nl9TGGLPoPKSeNox6Tl21zCIFUfh+1AwICbiuB9WttWDdgCRlGQbShPReKY375V9HPweGo64BI1xVkNEsqKbauO8JvTkRro+5Xleij/RTtwSGnTgEybJwXf2U3oFXTiUYZkwji6aQptCdbXN74RNK9Uzjb06evuD9Ep46KL8GjP9AYvQzS1jbvB0bg7sw/sef6P5xfP0xTCZOaF1fhw9LKlEm+TQh4QkjzIP7f3PxPPiBJKmMsRUAHwPwz621j3sryMJNFzw391a1DzS4GwBuvPHGS9zdnZCQkHC5YIONFWjJIBFU+gxEpDQijI7EuSCjmKwSWdyZFqFKxh2/DQ7Steh0YvbhAp1Mp8YlDkui9QEuITgml0LqjgJMs6pBnZUaQuhAuEWU/su1gQaRT596LE3oko3t0ISqyby9N/dfuxCkLd1aUykESTAA0Zyltu41oA0jIlBPaNXwTtjSIoK6FT0Xq4+kJsICTe83IBE+aZ3qmNlWRSWCuiJdMBK9iVD4dRW5v8eRCt9W60TW7qbtlKVtyNpL10PXmRlXl0M/kQP4HlbriCqpuw3cbOoi0j2wIliYp5ExeMZ0SDHOLW/vywXC7m23hKcCJhNgNEI5/gOsYYLj7DjW/wQofwzA9ATu+/wqDt/g/kgejYDP3TPBFc0mHnvsGEYjN4uaeGpCwhPDopqZ8roDaPcFAMZYBkdQf9da+3H/9GNk42WMPRsADdmfV4JcQkJCwtMBnNmOGkn21X44khAaxs95Zt722vhUXmBxPc00IqikngKYC/4BWpKycI0+fCfPVbD4kkpKkMKgjmy+/fUTgRWe1GZZE6mo80SHZlq7xNcExRRoA5JigrpTiZBIG9elLC0gRAKOpBa9lxYl+QKYU0/jXlI3t+lIHBFUDQTSCQBg8MqiwdAKDLySmFkGMISKlgwtoV4mtfQs35/d0P+eE/pqsGCApN5Tn2gMOz/7GuOQ5TjDou9/rzqHlOE4xXhgXUgU1dP0Lc/nC3NpZ1ITLiU2N8MM6sZH/TxcdhfKB+5D+U/ehjcdHmF06xiHbyjxuXsm+Aen3Fzqze8H3vY2t3siqQkJFw9n60y92HjSSSpzkulvAzhprb0jeumTAH4GwAf85z+Mnv9lxth9cIFJp9M8akJCwtMWLEq2Vd3wIa0EptMibBoIIJG6aBzUGB5IojE8KKgAQk8mhePQ/CGF4ww8W4uJGZFeStMdDmdhXbHyG2+/qNc1fm04rJBlDbgwvQAot11TZy78yZPTps4cGUbXbgwgXFs8W7lTiWDv1XDzlDFpBDyxpOoWNv9LU7CWsJO9152HQTILbVknHEky97nx58KCcwJ+1tUyABwFeCCvRJQpwZeUy2WBYOntI9TDREoy2XcBQEe23/6bEBQEFUPb7vMDx6Pd9tZ9EOK85oFl0BBhvpSIqgQLlTNyAdVVuyiw54Mn2JOacLlx7Nju83A7b0NZjjHGCHd9Zg3/7d9uYLQxxs3vLwMxTQQ1IeHi4snsTL0cSupNAI4C+AvG2Jf8c7fCkdMxY+znAHwdwFv9a58B8JMAHgGwg6dJcl5CQkLCIjBPZjg37v/gCoHokWV3Oi26tS09tZXURtq2ajIY3c4iAq3q2A/I0QCggYFgzkYq2zlSlxCrsLy8g5WVnc66iSg3dYambq2+fQVVaUc2hdRhvlYIAyYMrOaBoM6mg3CMqsoCQe1DSINqmoW50+1pFpRBsvf6SzonBFp7b9M7VUzktGkJXGVYJ/E2O4uoRwTUzZe2NuG5dF7/+gDt/OlyrrE8VOF7AbSkdFC4Nx22p5mzbPsj9meLQwKxZVjOqX/WqcQx6QZ8CFR008gWTT8nMQmn9R/yYU/Kz6cS8czAg3WZtqVDC38PGrb3xrxLnO6bcIlx1nm4m44hv34N7zbH8b6Pr+OGV5WJmCYkXELEnalrN65h48GNSxagdDnSff8Yi+dMAdcx1t/eAvilS7qohISEhH2KRQm9WglozKf3xnZYIqjTWY7tWQ4pDArMd4hqwwLxcEqjA6l2ReaI0XBgsTSsMBzOUBRNIKVUd9NHnikgU3Nr1EoAvdnaPojoxteltEC+gKQSqkZieyoDMVORwgmg0ymagYXAJCKl/dnT3Z47GxrYIGZn52ldHUSJvTEEXDcrJfZSjQzQpivHyikhnime+dfjgKW58/igJcAT8ZkI2y3a/nxRWKcQa9hgYxZwNmt3j+x5vXFwPkhK6v7GWefhHnPdqHd9dh3vev0G3r5RYlImopqQcCkRd6auv2L9ks2mXtZ034SEhISEvWFRbQuBnuvbf6fTAk2dhbTbqslCQA5ZP0mFq2qB7ZlA5WcTpZ87pGCepUJjeVhjaViHtOHhcIY8U3OKZhzeRL2mcVJvuCbtE3yBoP7G+xIxbeoM02kRrM514ypnak/n4jlUqtCJIYWThDPOUCxgMBJRYBK6JK4fgOTOwSB4S9gEtx0ySAQ4tvU2noTFxDieTaV8wENg7t73QPOnS4XGoHAElXv1ltTwdn3zZJVqdhzR7m5PKmqMkAgMhJqePubCoWAxgyPa4Tkf+nTIE9PY5rxEbw5YWpO7R5QMvBdYJCX1oOHECWB11XWj1hM3g/qKt5b41HddN+ro1jHw/kRUExIuFZ6sztREUhMSEhL2ESxYlGDbkokQNBSHEEl0FMemdsFJ1H9a1wLaRL2XkRWWSAzgiYxxya3LAlgetAQ1zxVyH2okhfH2XN2pvYnDjIic5p6gMq+4kpWX68W6VzUrQugTkdR+F6uueVAR4xnUOKm2Mz8a2UsJpI4K5shpZx5TA4AjqrSdJkJFnbHeHkv3kI6pDTDw2zYx+YrWkIFh5sOFhGVY8gR10OODgrX2Xmex1iFAS3ADLVwVUNUYVLWY+0Uff28b4625vD0uzbVqw4KdWyxQqcka3JjofkSvB3IZWZY1LJYtx8B/PYgqbCj8qTHwHJ2FvlTaby8wexd8E56CWF11Sb4PfGgTm485gnrbbcB4XCK/fowPLm/iY5uJpCYkXAqcrTP1YiOR1KcA/uKBB1CfOnVe2z568iR+5OqrL/GKEhISnrKw3bAkmkVdBNeBKqCmjiK4TtC2/1T7OhoAbcWIdfZeUvkGnl0I4UjqlUOF5WGDPNchIGk4rDpr6YYcCXDeDT6KH58LRnM0TYatM0udGdSqack3ETSlxcLr69teiUTGyCLlFHB25n5KruBuDpdU0jgoie6hFO2xB9Jge0FlDxFUHSpa2mMMvIX2GYwtJKcDaYKKDaBDUN0afP9sFOAkQs2MezOC7NsdhdYAVwwd8V0e1OH+iai7tg8pLCQsJGNzKnMDG+py4hnbZcs76jDN4dLjPgQAhBTjC2eb+UDg773wygveDwDwZ3vbLeGJ42wJosfKYxiPgZeOjmFtzXWitlUzJZ73uhLHXnfZlp6QcKBxthnxi41EUp8CqE+dOm/i+ZUHH7zEq0k4iPjqyZPnvW1+1VX4oZe+9BKuJuGJwEZ/qFOti0I3DInnrerVKopd8uaea8kLqX7atqRhELEGySyWBxpFrjEcNCiyBlJoLC9PO7bdalagiQhkq6wav54sBCEBTkHtg9TfmJTWzfxkphS62/kZXaMUpqO0Um8pIU4mJoI6kG1YVEFVLrI9vuipse44bTWP+3A3bXng7mJcQ6Ph51KZRcUMCssXzJwyXMmAq4r4uhgqwwJBfcaVUxRZ06nWEdKE7zV9Bhxxjmt26PvbtxCvSIvlocKh5QqFrywixT0sHi6Mydmb43tggYYDvqs1nmw+5FVTOp+yjqDSvC+9MUDJ0f1AqszbfrNdZnPPhXqm8dcnz+9N4ISnCE6cwOrzJUb/MUoQ/cQdGH35vXjglW8HHpugLEusrQHHjwMfum2C8vAmgPnZ1YSEhIuLs82IvwfvuajnSiQ1IeFpALO9fd5vhHzx29++xKtJeCKwls2FBMWdo0CrtBpve23VUj6niCnlyBuRBMBViKz4ShOqGolnH6XQKIomENQ8IqlGc8ymzsyZZe411quQ6UNHayJ7cFVl4TqaOkNdy0BI++QsoJnvfw3X54lkv04lJqhkcxWcoch1OA5ZiElRjH9xCn98wVslmnpTpTQYNBynKh5qZzSAihlIyzCwLNheCQUDvmeoceVKHVRgbRhkw7FUaFy5MsMVKzvI8ibMHAtpIIWG5ryjMId7oBlOq4ig0333nwfcW7gHNZaGFXJPUun7oA2HEQxVIwPRJ1szQAnPwr0RYDl2tCOXS/56FqUiD0TXTj1TPFT9UKAVXUnmye9egpSs/y9hH2F1FeVohPHdt7gE0e99DTb+8l6MX3I7nvf8I8Afj/DQ8hgbGyU+dNsEr3vmCA99fYwj11/uhSckJFxMJJKakJCQsM8RE09jOAR0IBjUCwpgYdprmD81LXlZFNQDOLKW5wpLwyrUw/TBhXH1MVmDgbcBAwjhR3Pn1xxaCwihobVA02ShUiYORqprGTpYF4QQQ6uWiAPoElWvcvbhApFYm1TsA4OWo33J7tqff507v7f6KsVC2BSpjhqtiioAXGkEMjCssCg4yR++oE5T3dbHKM1R5I5ErqzMwjljm7fSws/iinDt9P2OU3jj78KAu/MtDzSeecUUw0HduaZ+16zg/h7VotuRq9oqm+XMFctuaSxUTIVYMOuL9ntBqnPB2r5Vun97RUr33WcoS2A8RjkaYe0XX4jjO/dg/QVHsfmVdwJXAVctj/Hcr4+w+dtreM7OBv7lQ2N84FfLyPKbkJBwEJBIakJCQsI+AmM2zCA2dYY+TSTlEQCqJvMEyyCePQUQApKUZtjuMQCiJkTsSBWUwmBpWKEYVIEImai7lFAMKhSeoFrNF1p6+89pLUKwU0y+4seOMJLv1BNST1iJoM0q92tNShNUSABzpCg871XPItehwiWewZTCgAsLoc1iAuyJKIG6RHcqdxe3FAsqKllW++pibDWO1wYARaZQZECeq871k2oOtLPGbRBW+72mtcXzrY1pu1VdfU2N4aBGUbQKqjsHD5bfVqHuvkMQv/FB95iIKgUqxbO+ktkQMAW0P2PhZ43BKaoGbeiwfaIkNSmp+w5licnaa7AxvQfrw5dj4+/uxy3Pn2A0KvGmN5W45X9cw7Vbx3H7Z9fxI+8oMX41sLmZSGpCwkFCIqkJCQkJ+xhxiFIMSsIFHLmL50/j5FkK0SlYlzQRpLD+w6mome8wJWgt5hJ587xBljUQ0qCpJVSUMEyzqCqamxRCL+w+jcGFhZDGzeHSk6pVTutadsiZ9pU6gtu5GUp6noKRBLdzKbm5PyYX7k0BsrvGSbfx+eJzAQiBSZVtrasCPsFWzM+/EmmjdVa1wDOvmAZyKqQJNly6P6SeUs1OTNDp2IBXLlmrqArRpgMvD1xCc1E0KAYVjOFtTVGUkpx7lTmfs21LVD1lle7pdt0SaVJP6Z4XuW6VXrJh611UX+ZnVnHhsECiqPsQk0/cgVF9L8aDoyh/436Ud9+C0SMj3HL3GH90Arj6pg3c/m/X8b/+5Aby60vgcErzTUg4aEgkNSEhIWEfwVpnw5RA6DuNSSOAMEdI6JC3qF5Go7VUEiEQzCX4BoIjWpUxkLNeP6vR3AckmY6qqpWz8jJhAikN85DeShrbfJWfR9VKdIi3BEKYT+fYEUGtGjd7OyhUqNJZHiqvJGeBCHXnKE2w8+a5UwhjAsr7llQRq51dgtp/PDPu3vbrVQpue6S0a0Mme+/ArykOhopnjekeEEE9vZWH6yPyF1fJuPtow/PLw6bTrUrHj+8vgHBfOsjbh9pwVBB+Lrd7v5Zz3VHu+5DSAJESHRNUoL136gmyTPMEelYTLgMmE2xurGO8djvKN74TKCdhRvWxK+7D7659HK/9F2O8/M0l8rIE/ngEvGwMHE4sNSHhICGR1ISEhIR9hrqWqOEsnlLozuygVm14jtEskLeqdsE2RFCrHoEiW+aVQ4Urlitow7E9zTo22BhaCUA6ezEXBjJXEEKDaQHra2PivlQFiabOoCOiZzSHIRuymp9ZJSIs0FbMxAoi3QvqQnWhTk41FoqHdcdqqpQmbBeTwDiASSsenq9riVq34VOzSnasxFRHExOtJiKoAghdp0RQi8wEtVNEluQid2nFRJxJQdWKB9II3dYJ7Uzz8H2i6+yjb3MuMoMrV2adayc1litSYEWon6FQJqBV5/NchfUYzTCrZCecCgCWh+7eV7WYs/T21xS/1nnO0izvE0OaSd1n2NzEsVs+3Xp3aUZ1cxNffeHz8PYNR1A3NoCyLFG+bAx8ZzOR1ISEA4ZEUhMSEhL2IXamebBg6mgucDprZS4ibzQ3qXRbQUJ//K94AkVzkdSBCug5lbEoGtd5KruWTyKjpH4qP4eq0XaiCmHQwBFTLrq0oekl0sbHJ+WQbL6kHgPUi9oGQ9H8qCOhPHytjQIgQzoxEbS+Ah2DzsGFhdGsnUf1BJUqbToBQr37C+xOUFviLwFPVMly3EefqGrDcWZ7EMghgA7x7UDxzhqvXJlhOKgXkk+gVVPz3KU4UziWVqJzv+jNES5sUETjtcTzvdvTzBNTvpBIAwj1OKGSBgBFRDVeAd5LT6qz+yYldV/h2IIqmbLEBCVGo7YTtSzhvy5RJq9vQsKBQyKpCQkJCfsKDDvTPBCz2HoZ180A3orplayq4agMw8zzGJqPXI7SVolcEHmRQ+0Sdb3VNssbDIfVXJ0MdaAa09p7+6AUX/dYhGM0TQatRHfdPTtx5zhRmA8FBSnlbL5kXc1zjdoH1RrNILgLB1oa1h11khRoul4i2RLoKIXx2mKC6u4xC0ptrKKSgros2nqbOICqtQ47Qq00DzOxtC5tOATMXHLxzjQPa4qP2VVGW1K9yNYcg958oHswzJuO3doYDiE1TN2dE5ZC+4/2vhVAUKrDNXhiulTocM+U6tqlQwUSd58r68gp/RQszoY+N4qBwDU/eNXedv6zPZ404eLh4RO47/OrOHxDic1NT1Cvn+Crn9rE5sljGI9TYFJCwkFFIqkJCQkJ+wjGIHR2asNhtIGCaImlMIHAzioZLL5A21EJOIK6IufDbLiwgezEXaiksLnZ03kCGRPPWFXtByDF2wPoEFSaR6XnhdQL9+8Q1EDWPFHyJM8R1baOZWnoOkABr84K3lEQSTU0PjFYSANdt+SQQoniudNwLRHRihXU5ShBl1ROIoxEqKU3o9KbA4K3llulBYTqEv6WWHeJKanGAAJBp0Remj3tz/UuCtwaDisMhk7D1N5eHScuk8WcQPbkeHY2nt0V3AQFm9ZUNRLb01YFVpqFblQY7JrkO98Ae25UM4Wv/Zfv7mHPhMuBb219C5NHJyiv86zze1bx+qvfiDd/+B/h2Dt+C+X1E9STEd515xg3v79VVBMSEg4eEklNSEhI2FdgUR2I+6M/782lAo6g7lQipMwSqPpkmZQ9T04BqjtpgrJGXahk+TSaQwgNmauFtTJEVLlXKwHA1E5BjYOSaNumzlA387+GaO40h7P+xvOqjpizQFCJhDnSRtfRJWNLeR2Sidv7oD3ZlYDqqakSkIb7uV/esdW6NXSJKqmodH+FT/C9cqhQ5HpO5YxJHBc2JOaG+diIqFZNFq7HpTS3xLt/nEAec0dUY/WU7Lt0XHocK9Z5pjAYzsJ94tq4iiElUIf6na7qLLxS2zk/IjIN0yHQjpQrbE9lePNEW58sbbphXs7e61VYMBTtlxeENJO6f7CcLWP00RHGbxmjvK7EZAe489sWH3v17+E3PnwYoUttpgAAIABJREFUP/rjG3j7xhg3vz+l+SYkHHQkkpqQkJCwj2B7f6S7mhB0SGusoMb23kHP2ktW0eVBHZS2omhJaUyCTERKFxFUApGXeE5WCI08bzrHMJ5kEgENIUlSAwrIoyRhAqmLSrtQnzidllTIeNaSbL3hXkXHMlG9ynw/qEBVZaHWpmp4Jxm4yMxcYBLg5045wuzp8rAOM6Z1qMSZp0yxgtpH4at84muqaxkqcnZDnuuF+9P54tli+p6Rggq0SjelLvcTl+NjxTU58ZyrNhwKAkYzmBCe1KYyA46gEoioCubIagGAylJDr+wFFqZapJ7U/YRDxSH85lt+E6OPjrB24xo2HtzA+C1/gKUzE7wbx/G+j6/jhlclgpqQ8HRAIqkJCQkJ+wi2Fx4juInqUFqCul0LbPs/6JUPpFnOdUc5BYDlQY2VlVkgLrHaSIjJZVNngN9GRCTWag6ZN0FB1Yp3OlyZcMfWWoTjGcPnel5JyXTHmCeVJiTEGgwHNYqi6ajIcdgPVwY1skCcYstqXLHCe4S4bjJMZzm2ZzmqWrSWaAomiogvKag0d0ppyPH8q0MR1u7uKQvqY7xdTKql0HP9qDk3nblVIqpGMyi0M7YxaexbrxeFXw2HVQi0CgS1zoLVV0jdqb+J71c8t0zbAVEgk0BIIqakacD9PMZdqm5x7tMg7uqNK2QukKQCKThpv6G8rsTajWs4/kfHsf6KdZRLQP2FDdz12XW86/UbePtGiUmZiGpCwkFHIqkJCQkJ+wixkhqrb1UjQ6hP1biambj79KrCkah4PpALiytWdgJp4dx01NMYXJjOLCoR1DgkiXMDFmyg6ATtCKFhBYUROWWur6LG4T0AAOmIEM1FUpos2XeHXvnjynRmZuP9c3TJKdCGLwlP+Oj8Te3OsbU1CAQ17lt1x2gDi0hNHUiDpUKHe9tPzwUccax191cuqZDxdrS+eVU42kb5X94+NEtpEZH37jHn3gDo3W8p3Pc+rguiNxKaOnNvSvj7KoUBxS4RcRXQ4Gr+56fpWZcBBNs0ddbSvevWz7BAStsuWaf+K8321EeT7L77C5NHJ7jjjzZw9Jp1fPnLd2Lnm3fhp/7lJ/CKt5b41HdLjG8eYXTrGEiW34SEA41EUhMSEhL2GVRspY1IAHVVAi4UieCspyrMb66sOHKXZ81CwhKO50kkFwZ53nQIaUxYQyruguTYYKHVIiT8klIXBzKdC0IaFGiCskikCQBUT+GLbbuL1FOgO/dJJFdpgZ1pMUdQi0x1LKoEKSwKbsI2ea7D/Gh8LjpPjHhOlN4k0EqEX8oxcaUwpz7oHFK070iQqtmf5aXjxKQ1nj0OfbVEUJsMQrZhUsZwqOgYHfKbt4/VAit41WQhzCsmpIu6UWPVNCazlKB8obAMMCwpqfsFZ6ozGH10hPfdMMZtP1/iY7/+GN7ytftw7RuB225zdTP59WN8cHkTH9tMJDUh4SAjkdSEhISEfQTGHBnt/89bCoPKM5UiM+GP+yLXWB64uFelOYaDGsPhDDzqCV2kaAalzCtsMm+Q9UioVjzYVBnvKq3aH4c6UFUt0dRZCEyiNUOYOWITyCcAHVlYiXwNh1WHJAmfChynBBNi9TRGa8MFplM3/Vg1GepahF5ZKSyWB061VVpgOuv1uXKL5aHC8qBe2D1Kii2ATtIwF7bTQ0pvEChu0NRZqIChtGMiiTHouqjvNe6THfoUYyLvdL/p3gho5JlCRmTfB2HN/H3Q2s2fUgjWdFq4Y/R+6OI3NVqrtOx8D1ynaxHeWCFFVOn2DZWgkvYet+dxVutFycrng0RR9w+2m2088GNvwvO2HsIb3rSJl/7Sb+E1a2/Do1+7Dw98aBPPK48BKPG815U49rrLvdqEhIRLiURSExISEvYZKDEWwJzCR4FIAEIoEnVj5kAgeEXRoBg4MmO8wtm187rHRGI6Vl7/GuNibvsYQho0TUtQqRPVvdaSm77FOFZzDVlKo1oc3iPEgFfzahfywz3ZaxVV3iGlBJrt1H7elepR6D4OChVCgdxzDPGvzfj+9uc/KeCI6nLCOqMqmL4iKYWBkT2F2tum+0psfI/DjKnqBVABIZV3rjpGam/hdvdSwynm1cwR1SxrwowqrSvuryWC2rdm072n+7ozdao0/UwSiHAKbt2bLrsQVaUZChpt5Xujmyk4af/gWSvPwvO2XgB8/d143htvx9ph4D98DPj0ez6O5R8dX+7lJSQkPIlIJDUhISFhH4HBhkoT3VEM21lJCviJ028BYGlYddTSuNc0WzDsJ4SBzLvJt93Xz55i09RyIUHdDf0wHyE1uK+ZWRTo1F+HkVFvqtSu/kbxoBgTyPpL6mpdSzfT6/cdFI7U0+wugQsLoY2fZeWh7zSPFOaYGBNBjRXUfj1ObMFdZJV1CnGrBhPZlkKHazDC2YGN4TB1e7y6kZ39+0TVXXsG7c9LimuweWunxNJxSIXtrFWis35XK+SSketaYnuao2o4ioy2YZ251LPV+QAu/VcuSFI+X+QDget+8KoL3g8AvvCFPe2W8ETwjW8AR28D7rkd9anbcMXXTuHT7/G1M88tUR6+3AtMSEh4spBIakJCQsI+AmNd8kSJqUrxTmovzUgSKVkaVkE5BRD6ToXopr/GfaZx3ykAWMODmsqpB1XNEyvatpoWmE0Hbp1nIaj94CSC8Z2iSys7yPMmzLTS/CQpfXHgDyE+Vp/gqqknfX4ONSaodN8AzBE6oK12oZjZmHTGaqdTZ0WnQ7Sf4muEI33xvGd87fHneC3xLHFVuQReqtsR0oDXWVA+nWWYiLuZU2+N5kFJj0HJvLHl2K1bBGIaE1WtBKpZgapyycikSFO4VFzZoywL1TNSzKuccS1NYzDX9XshqGYKf/Vfvrvn/ROeZHzzm/ja0XU8WrwTX/zMKbz71cfxtZV1HL6hxGgEjMdIc6gJCU8TJJKakJCQsK/g/oLnwkKiVVOlbJNVBTchxIdIUZY3HUVx4OcWiYgGIiN0O5MoW4uvNe3rPCKuXJqFRLWpJXa2l85bPeV8nnBybpANGxTDCkJoMC0gtAlkde5YQneswyZvOmm/nfoU0yqdZOeN71t3jQYCBkLNW4bJ5hsTVFIRFyUlE9kkpZWr1i7bR6yCxvsS+qSevtd1k0H669styTgGzQ3HimhMTmMFNoQ49Yhq3chAUE9vDRYqpICr7JmFJbA5krql3HZZ9CMl/KGKzAAzXDCS3Xf/oH7ms7Fy7wa+dOXf4Rf+h4/gayvrWPnWBv6XN5Z429tKbG4mkpqQ8HRBIqkJCQkJ+woMSnNItAQhJkNEtJaGVUdxo1lUIdwsIiX1Nk0GY+aVSIKNiB3j5qwKary/qlt7r/KW3f5jYJ5o9deQ5U1H7dVwQbZCGNRkT6VeT59IS8gxb6Ft19TWtjh1VM+pnQC6PaOinQ2Nn3cHbi25u82P0nnbtXDU8AOXTTZ/zAX7SqEDUTTGEdClKEiKSGV8HrqmRSoqoaoyFEXTUU3je7AItF1tOKbTQeiWJYIaW3tj9ZQIaGOAWfRzFL8umEv6Xc5cRy3NCOPMrrdnVySSun+QX/cc/Ol7fxRHn7GBf/3/ruF///D78Ll7ShzZHgFHxigTQ01IeNogkdRLhL944AHUp06d17aPnjyJH7n66ku8ooSEhIMAYxm0aVNjRa/blAsbCCqlvAIIVSN53oQ5UwCdShhVy1AjE9eekKLan0GNrcFaC6haRq+RvbOtL+mD1gRgoeWUQpvi7QEy2rbnjdNr4+14bkLyL9CGCGnFYbQj+0vDujNT2ieKoR/UXwcpn3FokDEcOZyCSTbfpSElKnctwP0bQUonEVCqkInXQ6ST1hYqYbRAnisUg6qzRtRR+m80w3q2uV4pDKoqmyOofRVaaQFogRytCj6dDrAzLVA1ElVNbwK0BLUyDI2/pURAqR91S7HwxkP/dQoBGxRq7uf8fGGRSOp+wjfOfAN/nv8OvvGf1vDVX7sWa+vAkVeXwGNj4DubwOFEUhMSni5IJPUSoT516ryJ51cefPASryYhIeGgwFpgVskQ7tMHqV5xyislte4WdGR7aqM4h6IX9vMzqloLWMODbZTIIxDPVLbk7nxA1TfAYksyAFjBQ4ovJc/2100JuURUaT15rjCM6lsARzJ3gxTG8UsZHTc6B1Xl9FXUmGTS66TYAq72hgizY7otoaS03Jx3751A26na77dVtLaeTfhsBHU39Otl3GNHqolIN3WGnWmB6SzDrOr+SUEKap+gUheqFBZQDMp6YiocORXcug7aaMaaZnv3gkRS9w++eeab+OpgHf/Xh9+HtXVgY8PZe8uyTAQ1IeFphkRSExISEvYh+hZawNlWxQJy6Syz7fOqpzyS9dfZaLtEj5Q4azg0EKpqVNR/KqRBU0tYzaE1h4L0Ka8uFTbzs6FkNSbVtN/hGc7pt9N+BrXWBZgwgUzHabT9sKROV6tXWUlhpGoUIQ0K0XQ7SnWrTsroeLuBUnmJIE+nRVBR3fcknlHt1uzUWgbCFSu6sS2Xvm/x+eLr5HlbDwN0iaSQOsykAmirhqIkYSLAtH76XtH+AJBnyv1sSQAqVoolprwINTt17TpqpTTQdUyOLZRiwd5Ls6XKMig/e6oBFAxYkc7SSynA8Yy1+/mWWBBAfU5YAHusV024DHhm/mzc+5cb+L2Nq/DW6xXK8lgKTEpIeJoikdSEhISEfQZt2ML/ecezg1oJIFPIsq5lFnDEksimEAZZ1jgS1Ov5BNAJTrI+KdbK/pwnh6oz1HUWCOh0WgR1sygaDIYu8YbIcl1nQQXthybxiNARIYXuEtq+NVhI3SHBhJh8EWkjK3RMAjP4ec6pCIFDcdAUvRHQmemMCCopomTDNrolWFUjoU13bhiRoJznOliOKdyIumy799mRQVJ/6fyLwLmBRDcwK7bzKgDw97NuZCDwUri+1Sx388xCalRVFpRiACENmch9XIVEvacEp5qyoJ4CCPOpM+PeGFmRFssDjSLXc7U/jqjKkGB94bBJSd1HGKrn4Ddv/CmU03f//+2de5xkVXXvv796dc8DGR4+eF1HBCWKioA4qEkcFASCEBN8cLkGlURzjQpiNBKjxnyi0QuGqFGuBA0xGg0iysQYQHl4Y5TnIMzwEhDEkbkBDUHCzHTXY+WPvffpXTXVPdVDd9ep6fX9fOrTdc7Z55x1dlWtPr+z1l6bj196Nu86IwhUL5jkOIsPF6mO4zgjSj5Or9Fo0Wi0inGL+XjUXlKEsVOtUO3TLi8clNOM4rbTaVPPhG+zGebaTKIzTV9SrbVZEqe+qWbFmoK9049FTdvbmdjcsnm8ELQpOpqn6uYkodqMQhim0mzzar/9SEI/FZ1K/ZsEWS+TzVDRNt9WzareTjRrtFpTAivNYZtHvFM14X6Fk5IYBQrRWK9PP3dt2idFP7vW9ZBs3Gou1b5HnSoK1WpX2DJRK65lOqqVUPSoFsembrUdWDEWoqf9BGqXra0KmyZmrhQ9HS5SR4en7PwAJ7T+gt/66tnsdWL4PoR03yEb5jjOguMi1XEcZ4Sw7H67Vu0UEbilMTKYj1Fst6o0m3Uqm7vTYDvtSvdyHgmLRZJSVd9UDKnTqRRpwm2gHecatXaFLZvHaDbrhTgtxp+ONRkbnyimkIEp0dVmatxppd0pxGSl2imKOykrxlRvNAtBW621+wrGZrNOpT0l6ibjXJ1JoC5btnnGvk3Vb1OxpHz8bLsVUnbTMfM5SNN0QDlFGm6n0yVQa9UO1Uqnb2Gl/FyVRqeYkzTZBlOp0ACVdhgL268vCqEa6RW0ebpysLdd2NNqV6lkUev08CG/tiS8c6HaalWKYkn51DK1qvW9zuU1Y+lYuyu1t6sfYoGwLRM1JpqVrkrAg9IYr7HvAbvMej+AO67Zrt2cx8PmjXzyW+/nXe88w4Wp4yxyXKQ6jtPFPbffPnDbxooVPGfVqnm0xpmOJFBTNdhqrU2j3grptXEMYhIXzck6TeqFUExprr0RzFwItZmafiYJ1MnJOo1GsxgbmlJ20/jTXCBXKh3Gl2yh1ggpx13R2RQwa/QUWYrHVbV7upTWZK0rFbg5We87jU1zst4l2vJU2LHxiSLlOF17nmqcRNjynTZ1FZoqqgfXusdxpvGrKdW1UjU6bU0bcU2fWRKo6eFCPvdqKBA1NR9pi6kqwkmcpn6opmgxW4/pLcZypmvtKRqVyJfTd6LSCteXF5HKo+O95Cm4uUCtVmJxpFpKexYTzQotCxHVqmDZeLtv5d6UQpxSfCea4dXejoDoxJYWd93xH7Pf0RkKGx/eg//98nNZ9qzVgKtUx1nMuEh1HKeLzmOPDVyZ+saHHppna5xeKhVYtqTJip02MTbWnBIXPfNfVvpEpprNepEqmshTcDudCtUsIthuVboEaqddYXKyXoi3iS1jhQBKAjUVSKo3moyluVlrHWrxvK1mPQjWnszOsSUTWLuCqh3GsxTkam2CLXRXIO5UK3Rqlb4prEksQ4y+diqMVSZYvnxT3/5MQjWNAU0CdSbS+M12T2QvCdVe8ghq+ttI44BTm57Ky40s9TWPnvaSz5XbTxxPN/3PICSh2nud/WgPUJ2oVjVqWertsiXNIu25t+/S+larEqOyHWoddY3lHRRP9x0dbMmenHzuWVzYeA2N1Rd6RV/HWcS4SJ0FPvep4zjDpiJjxU6bugr/5GMWK3FKmBSdTBFNCOM62+0qlXYax7p1udTmZK0QJZ1OJR4rRQu7hTBQiNLCvpiu2xif6JpftZIKMbWnUolz6vUmKSioTHxWq23Go11qV0MF4WqnuIb8+lMqbp6q21vxNz9uvzlBU7Sy2VMEKhUpyqOKaX7TfuSCsVbtMFZvFRV9q7VgX5U2TG69b6PeKj7bXKB22pXiAUS7HVK5O50KS+LDAOJY3dQPvf2TClnlpGvKlWzfFOR4rZOT1a6pZnrFaa1nmpiUAgwUU8uk93n/1KptWlQLoRrWTRWfGoMwB+sWZoXPkzpa7LknnPaR1bzmjy/k48uu5+mvdJHqOIuVkRGpko4GPkGotXC+mX10oW3wuU8dxxk2lUqH5VGkJvJoZrXaLuYrTQJ1LGu76bGlNCfr1BvNrigqhBTYFM1MpAhmqgScFzOqVDosW76pa3oYgMb4BEt22tzVrpKJo+ZEnWos7NPpVBhfMlFEWpO4rlbbRVXiJHCZpBjL2qlmkdVMOCaBl6c2p+O0JkMV20JsZxo9T6etVju0Y1rx5s1jMZ25XswRCkHIddoaaO7OsXqrSO8NNjaL6V3SfKh5X40v2dI3JTsvCAVT6cdd0d/JOpMzVP1N58hFd+rDxORkrbA3XXMSqKFScZ8iSJnozLe3s6JJ+dynKaIcrsuK95PtWtecqGO0qFayuXYfnfaypsFoa9tz/m4vkqrADcDPzOy4eTtRCdnWfZmkMeALwCHAL4DXmtl92zru6tXAR1bztetX855XzrnZjuOMCCMhUuM/gU8DRwIbgOslrTGz24ZrmeM4zsJSqViRClqku9a6I4b1epN2u7rV/KhpuhnYOorajkWK0tQ0qX27EKmhOm8hfRpN6oQCR6HBVGXasRkqC+ektN9avUljaZzLs0c49ab0WhbV7fREK6tZum5+DdPRlRacHSufhzUJ1d6oaZ6aWqu2i215EaVWu0K10ukSYXmKdk5aV4tR0nqfSUE77UrxAAJC5DzNQQshDbrfGFXIRGiL4j9/Evf9qjnnQjUJ8lq1w0T6uCtGrdZhYrJKq61CpCaBmv7mAjUXsolGo71VqnMvKZW53xzA22IBIqmnAbcDT5jPk5SNAe/LTgUeNrP9JL0O+Bjw2kGOP3b7CRwz/n3Ah5Q4zmJlJEQqcBhwt5n9GEDSV4ATABepjuMsKhRvuHPx1m9sZr3RLIRoHo1LgkZZYaB2u8rE5jFg65RSCFHGFF1Nf2tx/zQVTUrRzSOgQFFkKE8nzQsz1RotGksnpvZptGhN1gqxmoRzc7JGazJMdZPs6r2u/Jr7RYN7CzSl/ZKgTdeeiiWlNN9ELkYhCtJqe2r8ZCZUAWp0ijYQBGoqatX7WRUVkWvtothVorA7i5CmtN30IELVTtc+vZWJ84JPk5vrXRHSWidcQ7XW6Rp/2m/M7Vi9RatVYdmSyWhzg9bmWl9ROtVvUwI1rwic0nyBrrlWW+3KVtWSHw/teRKpkvYGfgP4MHDGvJykvAxyX3YC8Kfx/UXAX0uSmc34gXz/Myewauc1XPPI8XNvteM4I8OoiNS9gJ9myxuAFw7JFsdxIl4JeAhoSoDkYy17K/UGsdgtUpWN88v3bU3Wtiq21IwFkipdkdjuiFel0j3mtDett9Mjjor9UuQu7ldrtLqEbaXaoTVRZ3JLo7AvjY3NrzOlNqdpY/KpbnIbm9S70pFTnySBWq12qFY7bNk8tpVAzSv45mK0V6D2i/K1qBZtqrVo41j8TLJ03N7pbpJ90D2eNC+SlCLn6QFC19Q0vbbUps6XCj7lc7tOUg1T4vSpsNTuVOi0RaPRKq6x3WkVEdBWu1JEU8O2rceopqhrTm+EOQnmVNk3XWf3sWYvXMfGa+z/K7vNej+Ajd/fZpO/At4D7LRdJxhtBrkvK9qYWUvSI8BuwM+nO+iWB29i1c43cs0jx/Oit14yxyY7jjNKaBsPtEqBpFcDrzCz343LrwcOM7O3Z23eDLw5Lj4TuHPAw+/ODA5zSJTNJrdn25TNprLZA4Pb9FQz86pj0yDpUQb3bwtNGb93Cbdt+yibbbPyD5IuJVzD9jBOd6mm88zsvHjc44Bjzeytkl4K/OFiGpM64H3ZrbHNhrh8T2zzi55jFfdvuy3nkCevkN22wdYu0KXMNWX7vcwWt3+4jLr9zzSzOXtoNyqR1A3APtny3sADeYP4j+O82R5Y0g1mdujjM29uKZtNbs+2KZtNZbMHymnTiHJnWfuxzJ+x27Z9lNm2QTCzo+fp0C8Gjpd0LEHMPkHSF83sf83T+crGNu/LsjYbJNWAnYGtJq3N798k3fDzRzsj+30b9d+L2z9cdgT75/J42578rBxcD+wv6WmSGsDrgDVDtslxHMdxnEWImZ1pZnub2UrCPcmVi0igwmD3ZWuAU+L7Ewl9VP70PcdxSsFIRFLjWIa3AZcRSp1/3sxuHbJZjuM4juM4i47p7ssk/Rlwg5mtAT4H/L2kuwkR1NcNz2LHcUaNkRCpAGb2LeBb83DoWacILwBls8nt2TZls6ls9kA5bRpFytyPbtv24baNMGZ2NXD1kM1YcPrdl5nZB7L3W4BXz/Kwo/59c/uHi9s/XObU/pEonOQ4juM4juM4juMsDkZlTKrjOI7jOI7jOI6zCFj0IlVSVdJNkr5ZAltWSLpI0h2Sbpd0eAlseqekWyWtl/RlSeMLfP7PS3pQ0vps3a6Svi3prvh3lxLYdFb83G6R9HVJK4ZpT7btDyWZpO2dgmHO7JH0dkl3xu/T/1koe3YkJB0d+/BuSe8dwvn3kXRV9E+3Sjotru/7m1Tgk9HeWyQdPM/2dfnzWNTl2mjXP8YCL0gai8t3x+0r59murXx7ifpsKx9fln5zFhfD9m+DUHYfOChl9ZWDUGZ/OqD9I+VzNYv78Jn6W9Ipsf1dkk7pd65eFr1IBU4Dbh+2EZFPAJea2QHA8xiyXZL2At4BHGpmBxKKIyx04YMLgN4pBN4LXGFm+wNXxOVh2/Rt4EAzey7wI+DMIduDpH2AI4H7F9CWvvZIWg2cADzXzJ4NnL3ANo08kqrAp4FjgGcBJ0l61gKb0QLeZWa/AqwC/iDaMN1v8hhg//h6M3DuPNvX688/BpwT7XoYODWuPxV42Mz2A86J7eaTfr596H02g48vS785i4SS+LdBKLsPHJSy+spBKKU/HYQR9bkXMPh9eN/+lrQr8EHghcBhwAc1SIDJzBbtizCv1xXAEcA3h2zLE4B7ieOEy/AC9gJ+CuxKKLL1TeCoIdixElifLd8J7BHf70GYN3KoNvVsexXwpWHbA1xEcOD3AbsP+TO7EHj5Qn9OO9ILOBy4LFs+EzhzyDZdQngQ0vc3CXwWOClrX7SbB1u6/DkgwqTotd7+I1QkPTy+r8V28+J7p/PtJemzfj7+FWXoN38trlcZ/duAdpfGB87C5lL6ygFtL60/HdD+kfS5DHgfPl1/AycBn83Wd7Wb7rXYI6l/BbwH6AzbEGBf4CHgb2MKxvmSlg3TIDP7GSHidT+wEXjEzC4fpk2RJ5vZRoD490lDtqeXNwH/MkwDJB0P/MzMbh6mHRnPAH41pqt8V9ILhm3QCJL+uSU2xHVDIaYdPR+4lul/kwtpc68/3w34TzNr9Tl3YVfc/khsPx9M59uH3mf9fDxwI+XoN2dxUSr/Nggl9IGDUlZfOQil9aeDsAP53Nn293Z9DotWpEo6DnjQzG4cti2RGnAwcK6ZPR94jIVPY+0ihuJPAJ4G7Aksk7SYJiufNZLeR0gH+tIQbVgKvA/4wLbaLiA1YBdCetS7gQslabgmjRz9+mso5dklLQe+BpxuZr+cqWmfdXNu8zT+fKZzL2Rfzta3L5ht/Xw8IV1ruvOX5jvo7HCM1HerbD5wUEruKwehtP50EBaBz53O3u26jkUrUoEXA8dLug/4CnCEpC8O0Z4NwAYzuzYuX0T4IQ6TlwP3mtlDZtYELgZeNGSbAP5d0h4A8e+DQ7YHCIPCgeOAky3mMwyJpxMc4M3x+703sFbSU4Zo0wbgYgtcR3iCu2DFnHYQNgD7ZMt7Aw8stBGS6oSbsy+Z2cVx9XS/yYWyeSt/TogWrJCU5gPPz13YFbfvDPzHPNiVztXPtw+7z2B6H1+GfnMWF6Xwb4NQUh84KGX2lYNQZn86CDuKz51tf2/X57BoRaqZnWlme5vZSsKg5SvNbGhRQjP7/8BPJT0zrnoZcNuw7IncD6x90vwjAAAJq0lEQVSStDRGvV5GOYpMrQFSZbBTCGNChoqko4E/Ao43s03DtMXM1pnZk8xsZfx+bwAOjt+xYfENwj9DJD0DaBDGVjiDcz2wf6wC2CD4rTULaUD0A58Dbjezv8w2TfebXAP8Tqz4t4owZGDjXNs1jT8/GbgKOHEau5K9J8b28/JgaQbfPtQ+i/Tz8bdRgn5zFh1D92+DUFYfOChl9pWDUHJ/Ogg7is+dbX9fBhwlaZcYTT4qrpuZ+RpkO0ov4KUMuXBStOMg4AbgFsJN/S4lsOlDwB3AeuDvgbEFPv+XCXn7TYLYOpWQj38FcFf8u2sJbLqbkG//w/j6v8O0p2f7fSxg4aRp+qcBfDF+j9YCRyzkZ7ajvIBjCdWj7wHeN4Tzv4SQonNL9l0/drrfJCHF59PR3nWEiobzbWPhzwnjl66Lv8+vJv8FjMflu+P2fefZpq18e1n6rJ+PL0u/+WtxvYbt3wa0sfQ+cBbXUjpfOaDdpfWnA9o/Uj6XWdyHz9TfhHotd8fXGwc5t+KOjuM4juM4juM4jjN0Fm26r+M4juM4juM4jlM+XKQ6juM4juM4juM4pcFFquM4juM4juM4jlMaXKQ6juM4juM4juM4pcFFquM4juM4juM4jlMaXKQ6jwtJbUk/lLRe0lclLZ3Hc/2ZpJfH96fP9lxx3qYrJT1B0hMlfS/a/ZtZm0sk7Zktny3piLm7CsdxHMdxnLlH0n/NwzHvk7Quvm6T9OeSxuK2PSVdNMO+KyS9da5tmglJSyR9V1J1Fvu8TdIb59MuZ/a4SHUeL5vN7CAzOxCYBH5/kJ2iYJzV98/MPmBm34mLpwOzFcTHAjeb2S+Bk4C/Aw4H3h1teiWw1sweyPb5FPDeWZ7HcZwRQtI+ku6VtGtc3iUuP1XSHpK+Ocvj+cMtx3F2JFab2XOAwwhzep4HYGYPmNmJM+y3AlhQkUqYj/NiM2vPYp/PA++YJ3uc7cRFqjOX/CuwH4CkM2KUcr2k0+O6lZJul/QZYC2wj6ST4tO59ZI+FttVJV0Q162T9M64/gJJJ0p6B7AncJWkqySdKumcZISk35P0l33sOxm4JL5vAksIkyh3JNUIwvesfAcz+wmwm6SnzFUnOY5TLszsp8C5wEfjqo8C58Xf/xnA38zykP5wy3GcUiDplZKulXSTpO9IenJc/0RJ35a0VtJnJf1E0u4zHcvM/osQjPhNSbvG+7r18XjPlnRdzK67RdL+BF/69LjuLEnLJV0Rz7lO0glx33R/+DeSbpV0uaQlcdt+0e6b435Pj+vfLen6eK4PZWYW93qSXhqjqhdK+pGkj0o6Odq5Lh3LzDYB90k6bA673nmcuEh15oQo8o4B1kk6BHgj8EJgFfB7kp4fmz4T+IKZPZ8gFD8GHAEcBLwgpt4eBOxlZgfGJ3d/m5/LzD4JPEB4srca+ApwvKR6bPLG3n0iLwZujO//AXgFcCnwp4QnfV+IjqqXtXFfx3F2XM4BVsWHai8BPh7X/zbBTyDpDZK+IemfYqT1bfGB3E2SrkmRWH+45ThOifgesCred30FeE9c/0HgSjM7GPg68D8GOVjMRrsX2L9n0+8DnzCzg4BDgQ2Eh3X3xIy7dwNbgFfFc64GPi5Jcf/9gU+b2bOB/yT4XoAvxfXPA14EbJR0VGx/GOGe8RBJvyapAexrZvdldj0POA14DvB64BlmdhhwPvD2rN0NwK8O0gfOwuAi1Xm8LJH0Q8KP+37gc4QbvK+b2WPxqdvFTP3wf2Jm18T3LwCuNrOHzKxFcES/BvwY2FfSpyQdDfxyJgPM7DHgSuA4SQcAdTNb16fprmb2aNznETP7DTM7lCBCjwO+Fp/iXSTp8Gy/BwmRW8dxdlDMrElI/T8HON3MJiU9DXjYzCaypgcC/5Nwc/RhYFO8+fsB8DtZO3+45ThOGdgbuEzSOoKPe3Zc/xKCaMXMLgUensUx1WfdD4A/lvRHwFPNbPM0+31E0i3Ad4C9gCfHbfea2Q/j+xuBlZJ2IgQtvh7t3BKDCUfF100EX3sAQbTuThC4Odeb2cbox+8BLo/r1wErs3Z+r1cyXKQ6j5c0JvUgM3u7mU3S33klHsve921nZg8TnnxdDfwB4WnXtjgfeAPTR1EBWtOMg/0A4WbzJIJjfBPwkWz7ONDP2TqOs2NxDLCRIEQB9gAe6mlzlZk9amYPAY8A/xTX+w2P4zhl5FPAX8fMtLcQ7mlg5nu1aYnCcSXwo3y9mf0DcDzhfumyacblnww8ETgkRlz/PbMnfxjYBmoz2CjgL7L7z/3M7HPx3OM9bfPjdrLlTjxHwu/1SoaLVGc++H+E8QpLJS0DXkUYr9rLtcCvS9pdoQrbScB345iIipl9DXg/cHCffR8FdkoLZnYtsA8hwvHlaey6kzDgvyCOmdjTzL5LKMTUAYxuJ/cMYP3Ml+w4zigj6SDgSMIQhXdK2gO/4XEcZ/TZGfhZfH9Ktv57wGsAYvrsLts6kKTlwGeAb8SAQr5tX+DHcUjWGuC59NyrRVseNLOmpNXAU2c6X0wt3hCHgiFpTGFmh8uAN0V7kLSXpCdFm6qSev32IPi9XslwkerMOWa2FrgAuI4gRM83s5v6tNsInAlcBdxMqKx7CSH94+qYRnxBbNPLecC/SLoqW3ch8G+9jjPjn4GX9qz7MPAn8f2XCdHYa4CzAeI41/0I6cyO4+yAxDFR5xLSfO8nFFA7mxApWLmdh/UbHsdxFpqlkjZkrzMIdTe+KulfgZ9nbT8EHCVpLVNZJI9Oc9yrYoGk6whDu97Sp81rgfXx3u0AQp2PXwD/plAI8yzCsK5DJd1AiKreMcA1vR54R0wR/j7wFDO7nFBb5AcxjfkipsTw5YRU5tnyYkIKslMSZGbDtsFx5gSFaSLOMbMrptm+B8FpHjmLY74KONjM3j9HZjqOUzIkvRl4mZm9Ni5XCTdjZxCGA7zFzO6W9AbgUDN7W2x3X1z+eb4tPty6BXhOHG/vOI5TKhTmOm2bWSvW4Tg3puCONLFQ5xlm9vr53MeZf1ykOiOPpBWEG8qbzezV22j7GuDSmEIyyLFfDXzbzHoH4juOswiID6oOMbM/2Wbj7n384ZbjOKUlDne6kJBVOQm81cyuH65Vc4OkNwF/N+hcqZKOBO7qqQrsDBkXqY7jOI4zA5J+18wGKeCW2vvDLcdxHMd5HLhIdRzHcRzHcRzHcUqDF05yHMdxHMdxHMdxSoOLVMdxHMdxHMdxHKc0uEh1HMdxHMdxHMdxSoOLVMdxHMdxHMdxHKc0uEh1HMdxHMdxHMdxSsN/AxXlQ466yt1JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "lag000, vario000, npp = gam(sim,-99999.9,99999.9,cell_size,cell_size,ixd=1,iyd=0,nlag=100,isill=1)\n",
    "lag090, vario090, npp = gam(sim,-99999.9,99999.9,cell_size,cell_size,ixd=0,iyd=1,nlag=100,isill=1)\n",
    "lag045, vario045, npp = gam(sim,-99999.9,99999.9,cell_size,cell_size,ixd=1,iyd=1,nlag=100,isill=1)\n",
    "lag135, vario135, npp = gam(sim,-99999.9,99999.9,cell_size,cell_size,ixd=1,iyd=-1,nlag=100,isill=1)\n",
    "    \n",
    "plt.subplot(131)\n",
    "hist_st(sim.flatten(),vmin,vmax,log=False,cumul=False,bins=20,weights=None,xlabel='Porosity (%)',title='Porosity') \n",
    "\n",
    "plt.subplot(132)\n",
    "pixelplt_st(sim,xmin,xmax,ymin,ymax,cell_size,vmin,vmax,'Porosity','X(m)','Y(m)','Porosity (%)',cmap)\n",
    "\n",
    "plt.subplot(133)\n",
    "plt.plot(lag000,vario000,'x',color = 'blue',label = '000')\n",
    "plt.plot(lag045,vario045,'x',color = 'red',label = '045')\n",
    "plt.plot(lag090,vario090,'x',color = 'green',label = '090')\n",
    "plt.plot(lag135,vario135,'x',color = 'orange',label = '135')\n",
    "plt.plot([0,1000],[1.0,1.0],color = 'black')\n",
    "plt.xlabel('Lag Distance(m)')\n",
    "plt.ylabel('Semivariogram')\n",
    "plt.title('Regular Grid Expeirmental Variograms')\n",
    "plt.ylim(0,1.2)\n",
    "plt.xlim(0,1000)\n",
    "handles, labels = plt.gca().get_legend_handles_labels()\n",
    "plt.gca().legend(handles[::], labels[::])\n",
    "\n",
    "plt.subplots_adjust(left=0.0, bottom=0.0, right=2.0, top=1.0, wspace=0.3, hspace=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I hope you find this code and demonstration useful. I'm always happy to discuss geostatistics, statistical modeling, uncertainty modeling and machine learning,\n",
    "\n",
    "*Michael*\n",
    "\n",
    "**Michael Pyrcz**, Ph.D., P.Eng. Associate Professor The Hildebrand Department of Petroleum and Geosystems Engineering, Bureau of Economic Geology, The Jackson School of Geosciences, The University of Texas at Austin\n",
    "On Twitter I'm the **GeostatsGuy** and on YouTube my lectures are on the channel, **GeostatsGuy Lectures**."
   ]
  }
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